Podcasts about azure machine learning

Troy Meink, a senior leader at the National Reconnaissance Office, is President-elect Donald Trump's choice to serve as secretary of the Air Force. The former and future commander-in-chief announced his pick Thursday on Truth Social. As the civilian head of the Department of the Air Force — which also includes the Space Force — Meink would be responsible for leading the service during a period of wide-ranging modernization. If confirmed by the Senate, he'd be expected to play a key role in deciding the future of the Next-Generation Air Dominance program. The department is also pursuing Collaborative Combat Aircraft, the B-21 stealth bomber, a Proliferated Warfighter Space Architecture, AI capabilities, modernized cyber and IT tools, and the DAF Battle Network, among other technologies. Meink has been serving as principal deputy director of NRO, a position he was appointed to during the first Trump administration in 2020. In that role, he was tasked with “overall day-to-day management of the NRO, with decision responsibility as delegated by the Director.” Federal agencies with top-secret workloads can now use OpenAI's GPT-4o through Microsoft's Azure for U.S. Government Top Secret cloud. Microsoft announced Thursday it received authorization for 26 additional products in its top-secret cloud environment, meeting Intelligence Community Directive (ICD) 503 standards and allowing agencies — particularly those in the intelligence community and Defense Department — to use them for the government's most classified information. Those added tools include Azure OpenAI Service — which provides Azure customers access to OpenAI's generative AI large language models — and Azure Machine Learning, among others. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

Zwischen Theorie und Praxis In dieser Folge ist Hervé zu Gast, ein Experte für Data Science, AI und Business Intelligence. Hervé erzählt von seinem Werdegang, beginnend mit seiner Promotion im Bereich Machine Learning bis hin zu seiner aktuellen Tätigkeit als Freelancer - und vor allem wie ich sein Blick auf die tatsächlichen Herausforderungen der Unternehmen verändert hat. Artur nutzt die Gelegenheit, um Fragen über die Herausforderungen und Entwicklungen in der Datenwelt, den Einsatz von KI in BI-Tools sowie die zunehmende Bedeutung von Power BI in Unternehmen zu stellen. Hervé teilt seine Erfahrungen mit Python, Azure Machine Learning und Microsoft Fabric, und gibt interessante Einblicke in die Rolle von KI in der Zukunft der Datenanalyse. Hervé Teguim ist promovierter Mathematiker und mit über 8 Jahren Führungs- & Businessverantwortung und Microsoft-Zertifizierungen bringt er umfassende Kenntnisse in der Entwicklung moderner Data Science & Business Analytics Lösungen mit. Mit internationaler Erfahrung in der Schweiz, Deutschland und Irland bringt er vielfältige und sehr breite Einblicke in Geschäftskulturen ein. Hervé ist ein leidenschaftlicher Reisender und Fußballfan. Auf dem Fußballfeld findet er Gemeinschaft und Freude am Spiel, was die Bedeutung von Zusammenhalt unterstreicht. Weiterhin legt Hervé viel Wert auf sein ehrenamtliches Engagement.

What kinds of problems are organizations solving with Machine Learning? In this episode, we explore a situation where a public works department was looking for more accurate information to predict future water levels based on rainfall to maintain water tank storage for balancing pressure and to prevent overflow flooding. Marathon data solutions consultants Brian Knox and Andy Yao, built a custom machine learning model and made the results available through Power BI reporting. We talk through some of the data hurdles the project presented, the tools they used, and how their work provided results the client could rely on. We touch on Azure ML environment and future integrations that will come with Power BI and ML.  Have you done any work in ML or predictive modeling? Did you get any good take-aways from today's podcast? Leave us some love ❤️ on LinkedIn, Twitter/X, Facebook, or Instagram.  The show notes for today's episode can be found at Episode 275: Machine Learning and Power BI. Have fun on the SQL Trail!

Today we're joined by Jay Emery, director of technical sales & architecture at Microsoft Azure. In our conversation with Jay, we discuss the challenges faced by organizations when building LLM-based applications, and we explore some of the techniques they are using to overcome them. We dive into the concerns around security, data privacy, cost management, and performance as well as the ability and effectiveness of prompting to achieve the desired results versus fine-tuning, and when each approach should be applied. We cover methods such as prompt tuning and prompt chaining, prompt variance, fine-tuning, and RAG to enhance LLM output along with ways to speed up inference performance such as choosing the right model, parallelization, and provisioned throughput units (PTUs). In addition to that, Jay also shared several intriguing use cases describing how businesses use tools like Azure Machine Learning prompt flow and Azure ML AI Studio to tailor LLMs to their unique needs and processes. The complete show notes for this episode can be found at twimlai.com/go/657.

Bumble yesterday announced that Lidiane Jones, who currently serves as chief executive at Slack, will succeed founder and current CEO Whitney Wolfe Herd, on Jan. 2. Wolfe Herd will become Executive Chair at that time, the company said in a press release yesterday.  Nasdaq listed Bumble, which will report its fiscal third-quarter earnings later today, is expected to cross a billion dollars in revenue this year.  “Early in my career, I was the target of online abuse and harassment. I lived in a perpetual state of anxiety; the internet felt like the Wild West, dangerous and toxic. I knew there had to be a better way: A kinder, more respectful internet,” Herd wrote in an exclusive blog for Forbes India in March this year.  With that as the founding principle, Herd started Bumble in 2014, with a four-member team and a two-bedroom apartment, my colleague Naini Thaker wrote in her awesome piece on Bumble's India plans in July. Before Bumble, Wolfe Herd was a co-founder of Tinder, which she sued for sexual harassment, according to Forbes magazine. Bumble went public in February 2021, raising $2.15 billion in a listing that saw the company's stock soar to $76, versus the listing price of $43, before closing at $70.31. That made Wolfe Herd, who was 31 at the time, the world's youngest self-made woman billionaire, worth $1.5 billion. She owns about a fifth of the company, according to Forbes. Since then, the stock has plummeted well below the listing price and currently trades at around $13. Under Wolfe Herd's leadership, Bumble has built itself brand recognition as a dating app that is serious about women's safety online, according to the company's press release yesterday. Jones, who will take on the CEO's role, has a B.S. in computer science from University of Michigan. She's had a stellar record, which started as an intern at Apple in 2002, according to her LinkedIn profile.  She then spent close to 13 years at Microsoft, where she was Group Product Manager for Azure Machine Learning when she left for Sonos, the high-end speaker maker. She was there for close to four years, including through the company's IPO, and was VP of software product management when she left for Slack. Last year, she was named to replace Slack co-founder and CEO Stewart Butterfield, who left Salesforce in January this year. Salesforce acquired Slack in a $27.7 billion deal in 2021. Ray Wang, founder and principal analyst at Constellation Research, told TechCrunch that Jones's move to Bumble makes a lot of sense, and would be a better fit for her skillset. It's not about moving from enterprise software to a dating app product, but rather that “Slack is no longer a growth play, but an integration play for Salesforce, and Lidiane's talents are better at Bumble for turnaround and growth,” Wang told TechCrunch. When she was named CEO of Slack, Fortune Magazine, after an interview with her, described Jones as a rare Brazil-born, Latin American tech CEO. She's also a mother, according to Bumble's press release yesterday. “As a woman who has spent her career in technology, it's a gift to lean on my experience to lead a company dedicated to women and encouraging equality, integrity and kindness, all deeply personal and inspiring to me,” Jones said in the press release.

In this episode, we take DevTunnel CLI for a spin. It's a nifty little command-line tool for opening tunnels from the cloud (or public Internet) to your locally running APIs. Also, Tobi asks Jussi an unexpected question.(00:00) - Intro and catching up.(03:32) - Community highlights.(04:28) - Show content starts. Community Highlights- Tim Heuer: Announcing C# Dev Kit for Visual Studio Code- Aaryan Arora: Integrating Power Apps with Azure Machine Learning & Open AI using Power AutomateShow links- DevTunnel CLI- DevTunnel CLI on GitHub- Give us feedback!SPONSORThis episode is sponsored by Sovelto. Stay ahead of the game and advance your career with continuous learning opportunities for Azure Cloud professionals. Sovelto Eduhouse – Learning as a Lifestyle - Start Your Journey now: https://www.eduhouse.fi/cloudpro

Welcome to the newest episode of The Cloud Pod podcast! Justin, Ryan, Jonathan, Matthew are your hosts this week as we discuss all things cloud and AI, as well as Amazon Detective, SageMaker, AWS Documentation, and Google Workstation.  Titles we almost went with (and there's a lot this week)

As a partner with OpenAI — the company that recently wowed the tech world and the general public with its DALL-E image generator and ChatGPT chatbot — Microsoft helped to make those generative AI tools possible. But Microsoft has long invested in developing its own artificial intelligence technologies, for internal and external customers alike. And even when AI is not the centerpiece of a specific software program, it's often driving how that tool — such as the company's Bing search engine — works. As corporate vice president of Microsoft's AI platform, Eric Boyd oversees product and technology teams that build artificial intelligence and machine solutions for the company's Azure platform and its AI services portfolio. Eric joins Sam and Shervin on this episode to talk about how Microsoft builds AI tools and embeds the technology in its various products, AI's potential for helping to expand people's creativity, and the democratization of AI. Read the episode transcript here. Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger. Stay in touch with us by joining our LinkedIn group, AI for Leaders at mitsmr.com/AIforLeaders or by following Me, Myself, and AI on LinkedIn. Guest bio: Eric Boyd leads the AI platform team within Microsoft's Cloud + AI division. This global organization includes Azure Machine Learning, Microsoft Cognitive Services, Azure Cognitive Search, and internal platforms that provide data, experimentation, and graphics processing units cluster management to groups across Microsoft. Boyd joined the company in 2009 to create the Silicon Valley Search Ads team. In 2011, he moved to Bellevue, Washington, to lead the Bing Ads Development team before taking on his current role in 2015. Before joining Microsoft, Boyd was the vice president of engineering at Mochi Media, an ads startup that was acquired by Shanda Games. Previously, he was vice president of platform engineering at Yahoo for 10 years. Boyd has a bachelor's degree in computer science from MIT. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials. We want to know how you feel about Me, Myself, and AI. Please take a short, two-question survey.

In episode 117 of our SAP on Azure video podcast we talk about latest improvements to efficiency in Microsoft datacenters, sustainability guidance for Azure Kubernetes Services, using availability zones for HA in Azure NetApp Files, Premium SSD v2 storage configurations for SAP HANA, SAP events on Azure Event Grid, GA of Power Apps Ideas and Calling the Microsoft Graph on behalf of the SAP-authenticated user. Then we have Domnic Benedict from SAP and Bartosz Jarkowski joining us to talk about integrating the SAP Digital Supply Chain with Microsoft services. The integration with Microsoft Teams via Adaptive Cards notifies suppliers and also enables collaboration on key information from IBP in Teams. The usage of Azure Machine Learning for SAP Digital Supply Chains allows customers to use individual ML models to do forecasting with IBP. https://www.saponazurepodcast.de/episode117 Reach out to us for any feedback / questions: * Robert Boban: https://www.linkedin.com/in/rboban/ * Goran Condric: https://www.linkedin.com/in/gorancondric/ * Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/

The team catches up with the developers of the Databricks Accelerator for Azure Purview to learn when, where, and why you might use it.   Media file: https://azpodcast.blob.core.windows.net/episodes/Episode441.mp3 YouTube: https://youtu.be/W9Dyb6E5eKk Resources: The Databricks to Purview Solution Accelerator Repo: microsoft/Purview-ADB-Lineage-Solution-Accelerator: A connector to ingest Azure Databricks lineage into Microsoft Purview (github.com) Demo Deployment Quickstart: Purview-ADB-Lineage-Solution-Accelerator/deploy-demo.md at release/2.1 · microsoft/Purview-ADB-Lineage-Solution-Accelerator (github.com) YouTube Video overview: Demoing the Azure Databricks lineage solution accelerator in Microsoft Purview - YouTube The OpenLineage Repo: OpenLineage/OpenLineage: An Open Standard for lineage metadata collection (github.com) OpenLineage + Purview Blog: Microsoft Purview Accelerates Lineage Extraction from Azure Databricks | OpenLineage   Other updates: Public preview: 128 vCore option for Azure SQL Database standard-series hardware | Azure updates | Microsoft Azure - 415 GB of memory   Azure Basic Load Balancer will be retired on 30 September 2025—upgrade to Standard Load Balancer | Azure updates | Microsoft Azure    https://azure.microsoft.com/en-us/blog/microsoft-and-int-deploy-ivaap-for-osdu-data-platform-on-microsoft-energy-data-services/   Azure Machine Learning—General availability updates for September 2022 | Azure updates | Microsoft Azure   Azure Machine Learning—Public preview updates for September 2022 | Azure updates | Microsoft Azure   Public preview: Azure Firewall Basic | Azure updates | Microsoft Azure  designed for SMB ; cost effective SKU   https://azure.microsoft.com/en-us/blog/strengthen-your-security-with-policy-analytics-for-azure-firewall/

In episode 104 of our SAP on Azure video podcast we talk about additional information on Azure Monitor for SAP Solutions, Microsoft Sentinel for SAP solutions, combining Azure Machine Learning with SAP Data Warehouse Cloud, embedding self-hosted SAP Fiori Launchpad into Microsoft Teams and then take a closer look with Uma Anbazhagan from SAP on how to integrate SAP Business Processes in Microsoft Teams using SAP Event Mesh and the Azure Bot Framework. https://www.saponazurepodcast.de/episode104 https://blogs.sap.com/2022/07/04/integrating-sap-business-processes-in-microsoft-teams-using-sap-business-technology-platform/" #SAPonAzure

Henk Boelman joins Scott Hanselman to discuss how AI can help to create more accessible experiences with Azure Machine Learning, Azure Cognitive Services, and Azure Media Services. They will look at the AI building blocks available in Azure and see how these building blocks can be applied. Chapters 00:00 - Introduction 03:32 - Henk's demo environment using Jupyter notebooks 05:28 - Computer Vision demo 10:42 - Custom Vision demo 16:59 - Face demo 20:20 - Handwriting example 20:59 - Text to speech example 22:38 - Bonus demo: Accessible stream with Azure Media Services 26:42 - Wrap-up Recommended resources hnky / azure-friday Computer Vision documentation Custom Vision documentation Face documentation Tech A11y Summit Connect Scott Hanselman | Twitter: @SHanselman Henk Boelman | Twitter: @HBoelman Azure Friday | Twitter: @AzureFriday

Henk Boelman joins Scott Hanselman to discuss how AI can help to create more accessible experiences with Azure Machine Learning, Azure Cognitive Services, and Azure Media Services. They will look at the AI building blocks available in Azure and see how these building blocks can be applied. Chapters 00:00 - Introduction 03:32 - Henk's demo environment using Jupyter notebooks 05:28 - Computer Vision demo 10:42 - Custom Vision demo 16:59 - Face demo 20:20 - Handwriting example 20:59 - Text to speech example 22:38 - Bonus demo: Accessible stream with Azure Media Services 26:42 - Wrap-up Recommended resources hnky / azure-friday Computer Vision documentation Custom Vision documentation Face documentation Tech A11y Summit Connect Scott Hanselman | Twitter: @SHanselman Henk Boelman | Twitter: @HBoelman Azure Friday | Twitter: @AzureFriday

In Episode 76 of the SAP on Azure Video Podcast we talk about the next virtual datacenter tour, working with Azure Machine Learning, Switching Active/Pasive Deployments across Availability Zones, upgrading Load Balancer SKU through PowerShell script, Azure Ultra Disk Storage in West US 3 and upcoming Microsoft Virtual Training Days. Then we have Philipp Leitenbauer joining us again to talk about the latest on the SAP on Azure Quality Check tool. https://github.com/Azure/SAP-on-Azure-Scripts-and-Utilities/tree/main/QualityCheck https://www.saponazurepodcast.de/ https://github.com/hobru/SAPonAzure https://youtu.be/OCOUtzRd60s

On The Cloud Pod this week, the team's collective brain power got a boost from guest hosts Rob Martin of the FinOps Foundation and Ben Garrison of JumpCloud. Also, AWS releases Data Exchange, Google automates Cloud DLP, and Azure Synapse Analytics is available for pre-purchase.  A big thanks to this week's sponsors: Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure. JumpCloud, which offers a complete platform for identity, access, and device management — no matter where your users and devices are located.  This week's highlights

It's about time for another news roundup from our editorial team. We look at new product launches, Microsoft Dynamics and Azure partner news, new sales figures, and some of the most popular article contributions from subject matter experts.   Show Notes: 0:45 - The launch of Microsoft Cloud for Financial Services set for Nov 1 2:00 - Report estimates Dynamics 365 Field Service closing in on $200 million in annual revenue 3:45 - Azure for healthcare: Opportunities abound in the cloud 5:00 - Microsoft to bolster Cloud for Healthcare with Truveta data platform investment & partnership 7:45 - Zuora to run subscription management services on Microsoft Azure, integrate with Dynamics 365 and Power BI 10:00 - Akerna expands ERP portfolio with $17 million acquisition of Microsoft ISV 365 Cannabis 12:15 - How Microsoft partners are building solutions with Azure Machine Learning 13:30 - Updates to Dynamics 365 media and entertainment accelerator adds Teams integration, content workflow support 14:30 - Microsoft's Power Apps Portals customers were exposing millions of private records, security firm reveals And related discussion at MSDW: Portals Community Call, September 2021: Securing Power Apps Portals Portals Community Call, August 2021: Using React with Power Apps Portals 19:15 - Features roundup How Microsoft customers should prepare for intelligent edge investment Microsoft Dynamics 365 Training Insights, Session 11: e-Commerce My favorite new features coming to Microsoft Dynamics 365 Sales, Marketing, and Customer Service in 2021 release wave 2 Power Platform 2021 Release Wave 2: Experts note developer improvements, simplified Dataverse access

This week on WPwatercooler we discuss the future of the Media Library with Helen Hou-Sandí and Joe McGill. “How do you fix the media library when people don't understand filesystems?” We've talked about the Media Library in WordPress quite a bit and when we saw Helen's tweet we just had to reach out. Joining us is Joe McGill who is one of the Media Library component maintainers. If you have been wanting more from the Media Library we hope this conversation helps with that. Create a classification model with Azure Machine Learning designer ClassifAI – Enhance your WordPress content with Artificial Intelligence and Machine Learning services BEC Sammich Panel Jason Tucker – jasontucker.blog Steve Zehngut – zeek.com Sé Reed – sereedmedia.com Jason Cosper – jasoncosper.com Helen Hou-Sandí – helen.blog Joe McGill – joemcgill.net Show Sponsors Desktop Server – ServerPress https://serverpress.com WPsitesync – https://www.wpsitesync.com Are You Looking For Brand Awareness? You could be a show sponsor. Let people know you're still in business and supporting your products. Supporting podcasts is a great way to repurpose your in-person conference budget. We have been sponsored by big brands such as Kinsta and Cloudways. Why not get your audience in front of the thousands of people who download this show every month? Yes, WPwatercooler has thousands of downloads every month. We're not just a YouTube Show. https://www.wpwatercooler.com/sponsor

Recorded live on September 10, 2021On this episode of the AI Show, Timm Walz joins Seth to talk about how ramping up your custom NLP tasks with Verseagility will benefit your data science workflow. He'll show us how to use the toolkit in combination with Azure Machine Learning. Be sure to check it out!Jump to:[00:27] Welcome Timm Walz[01:00] What is Verseagility[02:40] What does Verseagility include-standardized template for Infra as code-scripts-project conflicts[04:25] Demo[07:44] More about the data[11:42] Machine Learning tech stack[15:42] Learn more[16:35] Example ticketsLearn more:Github Microsoft Verseagility https://github.com/microsoft/verseagilityVerseagility Azure Websites https://verseagility.azurewebsites.netZero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeFollow Seth https://twitter.com/sethjuarezFollow Timm https://twitter.com/nonstoptimm/Don't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribeAI Show Playlist https://aka.ms/AIShowPlaylistJoin us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

Recorded live on September 10, 2021On this episode of the AI Show, Timm Walz joins Seth to talk about how ramping up your custom NLP tasks with Verseagility will benefit your data science workflow. He'll show us how to use the toolkit in combination with Azure Machine Learning. Be sure to check it out!Jump to:[00:27] Welcome Timm Walz[01:00] What is Verseagility[02:40] What does Verseagility include-standardized template for Infra as code-scripts-project conflicts[04:25] Demo[07:44] More about the data[11:42] Machine Learning tech stack[15:42] Learn more[16:35] Example ticketsLearn more:Github Microsoft Verseagility https://github.com/microsoft/verseagilityVerseagility Azure Websites https://verseagility.azurewebsites.netZero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeFollow Seth https://twitter.com/sethjuarezFollow Timm https://twitter.com/nonstoptimm/Don't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribeAI Show Playlist https://aka.ms/AIShowPlaylistJoin us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

We didn't know what to expect when we sat down with Greg Beaumont, Senior Business Intelligence Specialist at Microsoft specializing in serving Microsoft's Healthcare space customers' technical Power BI issues.  What we got was an insightful, delightful, and impactful conversation with a really cool and smart human! References in this Episode: The Game Azure Health Bot The Future Will Be Decentralized-Charles Hoskinson Spider Goats Episode Timeline: 3:10 - The magic of discovery with the Power Platform, It's all about the customers(and Greg has a LOT of customers!), and Greg's Data Origin Story 21:10 - The IT/Business Gap, Getting good BI and keeping data security is a tricky thing, The COVID Challenge hits Healthcare 43:00 - Power BI-Not just a data visualization tool, a very cool discussion on Genomics and using data to save lives, the importance of Data Modelling 59:10 - The Bitcoin Analogy, The VertiPaq Engine and when is Direct Query the answer 1:08:30 - We get a little personal with Greg, Azure/Power BI integration and Machine Learning, Cognitive Services and Sentiment Analysis Episode Transcript: Rob Collie (00:00:00): Hello, friends. Today's guest is Greg Beaumont from Microsoft. Like one of our previous guests, hopefully, Greg has one of those interface jobs. The place where the broader Microsoft Corporation meets its customers at a very detailed and on the ground level. On one hand, it's one of those impossible jobs. More than 100 customers in the healthcare space look to Greg as their primary point of contact for all things technical, around Power BI. That's a tall order, folks. And at the same time, it's one of those awesome jobs. It's not that dissimilar, really, from our job here at P3. Rob Collie (00:00:45): In a role that, first of all, you get broad exposure to a tremendous number of organizations and their problems, you learn a lot super, super quickly. When you're doing it right, your work day is just nonstop magic. The power platform is magic and not really because of the technology, but instead because of its impact on the people who use it, who interact with it, who benefit from it, whose lives are changed by it. And again, I can't stress this enough, software usually doesn't do this. And as we talked with him, Krissy and I just couldn't stop nodding, because we could hear it, he lives it, just like we do. And I hope that just leaps out of the audio for you like it did for us. Rob Collie (00:01:32): No surprises here, Greg didn't start his life as a data professional. He's our second guest on this show, whose original training was in biology. And so, some familiar themes come back again, that good data professionals come from a wide variety of backgrounds, that the hybrid tweeners between IT and business are really where the value is at today. And I love this about Greg, that we made a point of talking about how much easier it is today to break into the data profession than it's ever been and what an amazing thing that is to celebrate. Rob Collie (00:02:06): We talked about COVID and specifically its impacts on the industry. How that has served as a catalyst for many organizations to rethink their analytic strategy, the implications of remote work, data privacy and security. And of course, it wouldn't be an episode of Raw Data, if we didn't nerd out about at least one thing. So, we get a little bit into genomics and the idea of DNA and RNA as forms of biological computer code. And as you'd expect, and want, Greg is far from a one dimensional data professional, just such an interesting person, authentically human, a real pleasure to speak with, so let's get into it. Announcer (00:02:47): Ladies and gentlemen, could I have your attention, please. Rob Collie (00:02:51): This is the Raw Data by P3 adaptive podcast with your host, Rob Collie. Find out what the experts at P3 Adaptive can do for your business. Just go to p3adaptive.com. Raw Data by P3 Adaptive is data with the human element. Rob Collie (00:03:13): Welcome to the show, Greg Beaumont. How are you? Greg Beaumont (00:03:17): I'm doing well. How are you all? Rob Collie (00:03:19): I think we're doing pretty well. Greg Beaumont (00:03:19): Awesome. Rob Collie (00:03:20): Business is booming. Data has turned out to be relatively hot field, but I think it's probably got some legs to it. And the Microsoft platform also, well, it's just kind of kicking ass, isn't it? So, business wise, we couldn't be better. I think personally, we're doing well, too. We won't go into all that. What are you up to these days? What's your job title and what's an average day look for you? Greg Beaumont (00:03:39): So, I'm working in Microsoft and my title is Technical Specialist. And I'm a Business Intelligence Technical Specialist, so I focus almost exclusively on Power BI and where it integrates with other products within the Microsoft stack. Now, I'm in the Microsoft field, which is different from a number of guests you've had, who work at corporate and we're working on the product groups, which is that I'm there to help the customers. Greg Beaumont (00:04:01): And you hear a lot of different acronyms with these titles. So, my role is often called the TS. In the past, it was called a TSP. It's just a change in the title. Sometimes you might hear the title, CSA, Cloud Solution Architect. It's very similar to what I do, but a little bit different. But effectively from an overarching standpoint, our goal in the field as Technical Specialists is to engage with customers, so that they understand how and where to use our products, and to ensure that they have a good experience when they succeed. Rob Collie (00:04:29): Your job is literally where the Microsoft organism meets the customers. Greg Beaumont (00:04:34): Yep. Rob Collie (00:04:35): That's not the role I had. I was definitely on the corporate side, back in my days at Microsoft. I think the interaction between the field and corporate has gotten a lot stronger over the years. I think it's a bit more organic, that interplay, that it used to feel like crossing a chasm sort of thing. And I don't think that's really true anymore. Greg Beaumont (00:04:54): At a green, I think that's by design, too. So, with the more frequent release schedules and also kind of how things have changed under Satya, customer feedback drives the roadmap. So when these monthly updates come out, a lot of it is based off of customer demand and what customers are encountering and what they need. So, we're able to pivot and meet the needs of those customers much more quickly. Rob Collie (00:05:15): Yeah, you mentioned the changing acronyms, right? I mean like yes. My gosh, a thousand times yes. It's almost like a deliberate obfuscation strategy. It's like who's what? Why did we need to take the P off of TSP? I mean, I'm sure it was really important in some meeting somewhere, but it's just like, "Oh, yeah, it's really hard to keep track of." It's just a perpetually moving target. But at the same time, so many fundamentals don't change, right? The things that customers need and the things that Microsoft needs to provide. The fundamentals, of course, evolving, but they don't move nearly as fast as the acronym game. Greg Beaumont (00:05:52): Right. I think that acronym game is part of what makes it difficult your first year here, because people have a conversation and you don't know what they're talking about. Right? Rob Collie (00:06:00): Yeah, yeah, yeah. Greg Beaumont (00:06:00): And if they just spelled it out, it would make a lot more sense. Rob Collie (00:06:03): Krissy was talking to me today about, "Am I understanding what Foo means?" There's an internal Microsoft dialect, right? Krissy was like, "Is Foo like X? Is it like a placeholder for variable?" I'm like, "Yes, yes." She's like, "Okay. That's what I thought, but I just want to make sure." Krissy Dyess (00:06:18): That's why there's context clues in grade school really come into play when you're working with Microsoft organization, because you really got to take in all the information and kind of decipher it a bit. And those context clues help out. Greg, how long have you been in that particular role? Has it been your whole time at Microsoft or are have you been in different roles? Greg Beaumont (00:06:36): So, I should add, too, that I'm specifically in the healthcare org, and even within healthcare, we've now subspecialized into sub-verticals within healthcare. So, I work exclusively with healthcare providers, so people who are providing care to patients in a patient care setting. I do help out on a few other accounts, too, but that's my primary area of responsibility. Greg Beaumont (00:06:55): So, I started with Microsoft in 2016. I was actually hired into a regional office as what's called the traditional TSP role and it was data platform TSP. So, it was what used to be the SQL Server TS role. A few months later, the annual realign happened, I got moved over to Modern Workplace because they wanted to have an increased focus on Power BI, and I had some experience in that area. Plus, I was the new guy, so they put me into the experimental role. A year later, that's when they added the industry verticals and that's when I moved into what is kind of the final iteration of my current role. And the titles have changed a few times, but I've effectively been in this role working with healthcare customers for over four years now. Rob Collie (00:07:35): And so, like a double vertical specialization? Greg Beaumont (00:07:37): Yeah. Rob Collie (00:07:37): Healthcare providers, where there's a hierarchy here? Greg Beaumont (00:07:40): Yeah, yeah. Rob Collie (00:07:41): Those are the jaw dropping things for me is sometimes people in roles like yours, even after all that specialization, you end up with a jillion customers that you're theoretically responsible for. Double digits, triple digits, single digits in terms of how many customers you have to cover? Greg Beaumont (00:07:58): I'm triple digits. And that is one of the key differences from that CSA role that you'll see on the Azure team is they tend to be more focused on just a couple of customers and they get more engaged in kind of projects. And I will do that with customers, but it's just, it's a lot more to manage. Rob Collie (00:08:14): Yeah. What a challenging job. If you think about it, the minimum triple digit number is 100, right? So, let's just say, it's 100 for a moment. Well, you've got 52 weeks a year plus PTO, right? So, you're just like, "Okay." It is very, very difficult to juggle. That's a professional skill that is uncommon. I would say that's probably harder than the acronym game. Greg Beaumont (00:08:37): Yeah, there's been times I was on a vacation day and I got a call. I didn't recognize the number. I'm like, "Okay, I'm going to have to route this to somebody because I'm off today." And they're like, "Well, I'm the VP of so and so and we need to do this." And I'm like, "Okay, I got to go back inside and work now, because this is an important call." So, you have to be flexible and you're correct, that it makes it a challenge to have that work-life balance also, but the work is very rewarding, so it's worth it. Rob Collie (00:09:01): Yeah. It's something that vaguely I have a sense of this. I mean, transitioning from corporate Microsoft to, I mean, you can think of my role now as field. I'm much, much closer to the customers than I ever was at corporate. And yes, Brian Jones and I talked about it a little bit. And this is a bit of an artifact of the old release model that it was like every few years, you'd release a product, which isn't the case anymore. But that satisfying feeling of helping people, like even if you build something amazing back at Microsoft in the days that I was there, you were never really around for that victory lap. You would never get that feedback. It even never make it to you. Rob Collie (00:09:37): It was years later muted whereas one of the beautiful things about working closely with customers and our clients with Power BI, and actually the Microsoft platform as a whole, is just how quickly you can deliver these amazingly transformational like light up moments that go beyond just the professional. You can get this emotional, really strong validating emotional feeling of having helped. And that is difficult to get, I think even today, probably, even with their monthly release cycles, et cetera. By definition, you're just further removed from the "Wow" that happens out where the people are. Greg Beaumont (00:10:15): Yep. And I'm sure you all see that, too, with your business is that a lot of work often goes into figuring out what needs to be in these solutions and reports, but when you actually put it in the hands of leaders, and they realize the power of what it can provide for their business, in my case for their patients, for their doctors, for their nurses, it becomes real. They see it's actually possible and it's not just a PowerPoint deck. Rob Collie (00:10:38): And that sense of possibility, that sense of almost child-like wonder that comes back at those moments, you just wouldn't expect from the outside. I had a family member one time say, "Oh, Rob, I could never do what you do." Basically, it was just saying "How boring it must be, right?" It's so boring working with software, working with..." I'm like, "Are you kidding me? This is one of the places in life where you get to create and just an amazingly magical." It's really the only word that comes close to capturing it. You just wouldn't expect that, right? Again, from the outside like, "Oh, you work in data all day. Boring." Greg Beaumont (00:11:17): I'd add to that, that I'd compare it to maybe the satisfaction people get out of when they beat a game or a video game. That when you figure out how to do a solution and it works and you put in that time and that effort and that thought, there's that emotional reward, you get that I built something that that actually did what they wanted it to do. Rob Collie (00:11:35): Yeah. And after you beat the video game, not only did that happen, but other people's lives get better as a result of you beating this game. It's just like it's got all those dynamics, and then some. All these follow on effects. Greg Beaumont (00:11:46): It's like being an athlete and enjoying the sport that you compete in. Rob Collie (00:11:50): Yeah. We're never going to retire. We're going to be the athletes that hang on way too long. Greg Beaumont (00:11:56): Yep. Rob Collie (00:11:58): So, unfortunately, I think our careers can go longer than a professional athletes, so there's that. I can't even really walk up and down stairs anymore without pain, so. So what about before Microsoft? What were you up to beforehand and how did you end up in this line of work in the first place? Greg Beaumont (00:12:15): Sure. And I think that's actually something where listeners can get some value, because the way I got into this line of work, I think today, there's much more opportunity for people all over the world from different socioeconomic backgrounds to be able to break into this field without having to kind of go through the rites of passage that people used to. So, I was actually a Biology major from a small school. Came from a military family. I didn't have corporate contacts or great guidance counseling or anything like that. My first job right out of school was I said, "Oh, I got a Biology major. I got a job at a research institution." They're like, "Okay, you're going to be cleaning out the mouse cages." And it was sort of $10.50 an hour. Greg Beaumont (00:12:53): So, at that point, I said, "Okay, I got to start thinking about a different line of work here." So, I kind of bounced around a little bit. I wanted to get into IT, but if you wanted to learn something like SQL Server, you couldn't do it unless you had a job in IT. As an average person, you couldn't just go buy a SQL Server and put it in your home unless you had the amount of money that you needed to do that. Side projects with Access and Excel. Small businesses did things probably making less than minimum wage and side gigs, in addition to what I was doing for full-time work to pay the bills. Eventually caught on with a hospital where I was doing some interesting projects with data using Access and Excel. They wouldn't even give me access to Crystal Reports when we wanted to do some reporting. That was really where I kind of said , "Data is where I want to focus." Greg Beaumont (00:13:41): We did some projects around things like Radon Awareness, so people who would build a new house now, they're like, "Oh, I have to pay $1500 for that Radon machine down in the basement." But when you talk to a thoracic surgeon and their nursing team and you hear stories about people who are nonsmokers, perfectly healthy, who come in with tumors all over their lungs, you realize the value there and by looking at the data of where there's pockets of radon in the country reaching out to those people has value, right? I think it's that human element where you're actually doing something that makes a difference. So, that kind of opened my eyes. Greg Beaumont (00:14:14): I then after that job, I got on with a small consulting company. I was a Project Manager. It was my first exposure to Microsoft BI. It was actually ProClarity over SQL Server 2005 and we were working with data around HEDIS and Joint Commission healthcare performance measures for one of the VA offices. So, I was the PM and the Data Architect was building the SSIS packages, built out kind of skeleton of an analysis services cube. He asked me to lean in on the dashboarding side, and that's also where I started learning MDX because we were writing some MDX expressions to start doing some calculations that we were then exposing in ProClarity. And at that point, it was like, "This is magic." Greg Beaumont (00:14:57): From a used case perspective, what they were doing traditionally doing was they'd send somebody in from some auditing agency, who would look at, I think it was 30 to 60 patient records, for each metric and then they take a look at where all of the criteria hit for that metric, yes or no. And it would be pass/fail, how good is this institution doing of meeting this particular expectation. So, it would be things like, "Does a patient receive aspirin within a certain amount of time that they've been admitted if they have heart problems?" Something like that. With looking at it from a data perspective, you can look at the whole patient population, and then you could start slicing and dicing it by department, by time of day that they were admitted, by all of these different things. Greg Beaumont (00:15:38): And that's when I kind of said, "This is really cool, really interesting. I think there's a big future here." And I kind of decided to take that route. And from there, I got on with a Microsoft partner, where I stayed for about six years. And that's kind of where I was exposed to a lot of very smart, very gifted people. And I was able to kind of learn from them and then that led to eventually getting a job at Microsoft. But to make a long story short, today, you could go online and get Power BI Desktop for free. There's training resources all over the place, and you could skill up and get started and get a great job. I'd like to tell people take the amount of time you spend every night playing video games and watching television, take half that time and devote it to learning Power BI and you'll be amazed at how far you get in six to 12 months. Rob Collie (00:16:24): That's such good advice. I'm not really allowed to play a lot of video games, so I might need more time than that. But I had my time to do that years ago, learning DAX and everything. A couple of things really jumped out at me there. First of all, you're right, it was almost like a priesthood before. It was so hard to get your foot in the door. Look, you had to climb incrementally, multiple steps in that story to just get to the point where you were sitting next to the thing that was SSIS and MDX which, again, neither of those things had a particularly humane learning curve. Even when you got there, which was a climb, you get to that point and then they're like, "And here's your cliff. Your smooth cliff that you have to scale. If you wanted a piece of this technology," right? Rob Collie (00:17:11): You wanted to learn MDX, you had to get your hands on an SSAS server. The license for it. And then you had to have a machine you could install it on that was beefy enough to handle it. It's just, there's so many barriers to entry. And the data gene, I like to talk about, it does. It cuts across every demographic, as far as I can tell, damn near equally everywhere. Let's call it one in 20. It's probably a little less frequent than that. Let's call it 5% of the population is carrying the data gene and you've got to get exposure. And that's a lot easier to get that exposure today than it was even 10 years ago. Greg Beaumont (00:17:50): I'd completely agree with that. The people in this field tend to be the type of people who likes solving puzzles, who like building things that are complex and have different pieces, but who also enjoy the reward of getting it to work at the end. You've had several guests that have come on the show that come from nontraditional backgrounds. But I'm convinced that 20 years ago, there were a lot of people who would have been great data people, who just never got the opportunity to make it happen. Greg Beaumont (00:18:14): Whereas today, the opportunity is there and I think Microsoft has done a great job with their strategy of letting you learn and try Power BI. You can go download the dashboard in a day content for free and the PDF is pretty self-explanatory and if you've used excel in the past, you can walk through it and teach yourself the tool. I think the power of that from both the perspective of giving people opportunity and also building up a workforce for this field of work is amazing. Rob Collie (00:18:42): Yeah. I mean, all those people that were sort of in a sense like kind of left behind, years ago, they weren't given an avenue. A large number of them did get soaked up by Excel. If they're professionally still active today, there's this tremendous population of Excel people if they were joining the story today, they might be jumping into Power BI almost from the beginning, potentially. And of course, if they were doing that, they'd still be doing Excel. But there's still this huge reservoir of people who are still tomorrow, think about the number of people tomorrow, just tomorrow. Today, they're good at Excel and tomorrow, they will sort of, they'll have their first discovery moment with Power BI. The first moment of DAX or M or whatever, that's a large number of people tomorrow who are about to experience. It's almost like did you see the movie The Game? Greg Beaumont (00:19:36): I have not. Rob Collie (00:19:37): There's this moment early in the movie where Michael Douglas has just found out that his brother or something has bought them a pass to the game. And no one will tell him what it is. He meets this guy at a bar who says, "Oh, I'm so envious that you get to play for the first time." Also, this is really silly, but it's also like the ACDC song For Those About To Rock, We Salute You. For those about to DAX, we salute you, because that's going to happen tomorrow, right? Such a population every day that's lighting up and what an exciting thing to think about. Do you ever get down for any reason, just stop and think, "Oh, what about the 5000 people today who are discovering this stuff for the first time." That is a happy thing. Greg Beaumont (00:20:16): Yeah, I actually had a customer where one of their analysts who turned out to be just a Power BI Rockstar, he said, "I'd been spending 20 years of my life writing V-lookups, and creating giant Excel files. And now, everything I was trying to do is at my fingertips," right? And then within a year, he went from being a lifelong Excel expert to creating these amazing reports that got visibility within the organization and provided a ton of value. Rob Collie (00:20:42): And that same person you're talking about is also incredibly steeped in business decision-making. They've been getting a business training their whole career at the same time. And it's like suddenly, you have this amazingly capable business tech hybrid, that literally, it just like moved mountains. It's crazy. We've talked about that a lot on the show, obviously, the hybrids, just amazing. And a lot of these people have come to work for us. Rob Collie (00:21:09): That's the most common origin story for our consultants. It's not the only one. I mean, we do have some people who came from more traditional IT backgrounds, but they're also hybrids. They understand business incredibly well. And so, they never really quite fit in on the pure IT side, either. It's really kind of interesting. Greg Beaumont (00:21:26): Yeah, I think there's still a gap there between IT and business, even in kind of the way solutions get architected in the field. It's understanding what the business really wants out of the tool is often very different from how IT understands to build it. And I think that's where people like that provide that bridge, to make things that actually work and then provide the value that's needed. Rob Collie (00:21:47): There's such valuable ambassadors. It's just so obvious when IT is going to interact with a business unit to help them achieve some goal. It's so obvious that, of course, who you need to engage with IT. IT thinks, "We need to engage with the leaders of this business unit." They've got the secret weapon, these hybrid people that came up through the ranks with Excel. The word shadow IT is perfect. These people within the business, like they've been Excel people for their entire careers, they have an IT style job. Rob Collie (00:22:22): Almost all the challenges that IT complains about with working with business, you take these Excel people and sort of put them in a room where they feel safe. They'll tell you the same things. They're like, "I had exactly the same problems with my 'users,' the people that I build things for." And yeah, there's such a good translator. And if the communication flows between IT and business sort of through that portal, things go so much better. That's a habit. We're still in the process of developing as a world. Greg Beaumont (00:22:51): Yeah. And in healthcare that actually also provides some unique challenges. With regulation and personal health information, these Excel files have sensitive data in them, and you have to make sure it's protected and that the right people can see it. And how do you give them the power to use their skills to improve your organization, while also making sure that you keep everything safe. So, I think that's a hot topic these days. Rob Collie (00:23:15): Yeah. I mean, it's one of those like a requirement, even of the Hello World equivalent of anything is that you right off the bat have to have things like row level security and object level security in place and sometimes obfuscation. What are some of the... we don't want to get to shop talky, but it is a really fascinating topic, what are the handful of go-to techniques for managing sensitive healthcare information? How do you get good BI, while at the same time protecting identity and sensitivity. So often, you still need to be able to uniquely identify patients to tie them across different systems, can identify them as people. It's really, really, really tricky stuff. Greg Beaumont (00:24:02): And I think just to kind of stress the importance of this, you can actually go search for look up HIPAA wall of shame or HIPAA violation list. When this information gets shared with the wrong people, there's consequences and can result in financial fees and fines. And in addition to that, you lose the trust of people whose personal information may have been violated. So, I think a combination of you said things row level security and object level security as a start, you can also do data masking, but then there's issues of people export to Excel. What do they do with that data afterwards? Greg Beaumont (00:24:37): And then there's going to be tools like Microsoft Information Protection, where when you export sensitive information to Excel, it attaches an encrypted component. I'm not an MIT expert. I know how it works. I don't know the actual technology behind it. But it attaches an encrypted component where only people who are allowed to see that information can then open that file. So, you're protecting the information at the source and in transit, but you're still giving people the flexibility to go build a report or to potentially use data from different sources, but then have it be protected every step of the way. Greg Beaumont (00:25:11): So like you said, without getting too techie, there's ways to do it, but it's not just out of the box easy. There's steps you have to go through, talk to experts, get advice. Whether it's workshops or proof of concepts, there's different ways that customers can figure that out. Rob Collie (00:25:28): Yeah. So because of that sort of mandatory minimum level of sensitivity handling and information security, I would expect, now that we're talking about it, that IT sort of has to be a lot more involved by default in the healthcare space with the solutions than IT would necessarily be in other industries. Another way to say it, it's harder for the business to be 100% in charge of data modeling in healthcare than it is in other industries. Greg Beaumont (00:26:02): Yep. But you can have a hybrid model, which is where the business provides data that's already been vetted and protected and there might be other data that doesn't have any sensitive data in it, where it's game on, supply chain or something like that. But having these layers in between, the old way of doing things was just nobody gets access to it. Then there was kind of canned reporting where everybody gets burst in the reports that contain what they're allowed to see. But now, you can do things in transit, so that the end users can still use filters and build a new report and maybe even share it with other people. And know that whoever they're sharing with will only be able to see what they're allowed to see. It gets pretty complex, but it's definitely doable and the customers that are doing it are finding a lot of value in those capabilities. Rob Collie (00:26:48): That's fundamentally one of the advantages of having a data model. I was listening to a podcast with Jeffrey Wang from Microsoft and he was talking about it. And I thought this was a really crisp and concise summary, which is that the Microsoft Stack Power BI has a model-centric approach to the world whereas basically, all the competitors are report centric. And what does that mean? Why does that even make a difference? Well, when you build a model, you've essentially built all the reports in a way. You've enabled all of the reports. You can build many, many, many, many, many like an infinite number of different reports based on emerging and evolving business needs without having to go back to square one. Rob Collie (00:27:28): In a report-centric model, which is basically what the industry has almost always had, almost everywhere, outside of a few notable examples, Power BI being one of them. When a report centric model, every single change, I remember there being a statistic that was just jaw dropping. I forget what the actual numbers were, but it was something like the average number of business days it took to add a single column to a single existing report. It was like nine business days, when it should just be a click. And that's the difference. And so, preserving that benefit of this model centric approach, while at the same time, still making sure that everyone's playing within the right sandbox that you can't jump the fence and end up with something that's inappropriate. Very challenging, but doable. Greg Beaumont (00:28:15): Yep. That reminded me of an old joke we used to tell in consulting and this was back in the SharePoint Performance Point with Analysis Services days is there be a budget for a project, there'd be change requests along the ways, they discover issues with the data. And at the very end of the project, they rushed the visualization to market. And they're like after six months, with 10 people dedicated on this project, "Here's your line chart." Rob Collie (00:28:39): Yeah. I had a director of IT at a large insurance company one time, looking me in the eye and just brutally confess. Yeah, my team, we spent three months to put a dot on a chart. And that's not what you want. Greg Beaumont (00:28:59): Right, right. Rob Collie (00:29:01): That was unspoken. This was bad. To the extent that you're able to tell, what are some of the interesting things that you've seen in the healthcare space with this platform recently? Anything that we can talk about? Greg Beaumont (00:29:15): Yeah, so I think I'd start with how everything changed with COVID. Just because I think people would be interested in that topic and kind of how it changed everything. I actually had a customer yesterday at a large provider who said, "COVID was the catalyst for us to reconsider our investment in analytics, and that it spurred interest from even an executive level to put more money into analytics because of the things that happened." So obviously, when it hit everybody was, "What in the world is going on here?" Right? "Are we even going to have jobs? Is the whole world going to collapse or is this just going to be kind of fake news that comes and goes?" Everybody was unsure what was going on. Greg Beaumont (00:29:50): At the same time, the healthcare providers, a lot of them were moving people to work from home and these were organizations where they had very strict working conditions because of these data privacy and data security considerations, and all of a sudden, you're in a rush to move people home. So, some of my counterparts who do teams, they have some just amazing stories. They were up all night helping people set up ways to securely get their employees to a work-from-home type experience, so that they only had essential workers interacting with the patients, but then the office workers were able to effectively conduct business from home. Greg Beaumont (00:30:25): Additionally, there were use cases that were amazing. So, Microsoft has now what's called the Cloud for Health where we're effectively taking our technology and trying to make it more targeted towards healthcare customers and their specific needs, because we see the same types of use cases repeat from customer to customer. One of those use cases that came out of COVID was called Virtual Visits. And I actually know the team that built that solution, but because of patients who were on COVID, they didn't know how contagious it was. Greg Beaumont (00:30:56): There were people being put on ventilators, who weren't allowed to see their families and they were setting up a team's application, where people were actually able to talk to their family and see their family before they went under, right? There were chaplains who were reading people their last rites using video conferencing, and things like that. So, it was pretty heavy stuff, but I think from a healthcare perspective, it showed the value technology can provide. Greg Beaumont (00:31:21): And from our perspective in the field, it's like we're not just out there talking about bits and bytes. It kind of hit home that there's real people who are impacted by what we're doing and it adds another kind of layer of gravity, I'd call it, taking what you do seriously, right? I had another customer, they were doing some mapping initiatives with some of the COVID data because they wanted to provide maps for their employees of where the hotspots were. Greg Beaumont (00:31:46): And we were up till I think 11:00 at night one night working through a proof of concept. And they said, "Yeah, what's next is we also want to start mapping areas of social unrest." I said, "Wow, social unrest. Why are you worried about that?" And they said, "Well, we expect because of this lockdown, that eventually there's going to be rioting and issues in all different parts of the world." And at that time, I just kind of didn't really think about that, but then a lot of those things did happen. It was kind of just interesting to be working at night and hearing those stories, and then seeing how everything kind of unfolded. Greg Beaumont (00:32:18): Another example, look it up, there's an Azure COVID Health Bot out there and then there's some information on that, where you can ask questions and walk through your symptoms, and it will kind of give you some instructions on what to do. Another one that is even popular now is looking at employees who are returning to work. So, when people return to work find out vaccination status, "Are you able to come back to work? Are you essential? Are you nonessential?" I don't think a lot of customers were prepared to run through that scenario when it hit. Greg Beaumont (00:32:48): So, having these agile tools where you can go get your list of not only employees, but maybe partners that refer people to your network, because you might not have all the referring doctors in your system. So with Power BI, you can go get extracts, tie it all together and then build out a solution that helps you get those things done. I'd say it was eye opening. I think for customers and also for myself and my peers, that we're not just selling widgets. We're selling things that make a difference and have that human perspective to it. Rob Collie (00:33:20): Yeah, that does bring it home, doesn't it? That statement from an organization that COVID was the catalyst, evaluating and investing in their analytic strategy? Greg Beaumont (00:33:29): Yep. Rob Collie (00:33:30): Being in BI, being an analytics is one of the best ways to future proof one's career because at baseline, it's a healthy industry, there's always value to be created. But then when things get bad, for some reason, whatever crisis hits, it's actually more necessary than ever because when you've been in a groove when a an industry or an organization has been in an operational groove for a long time, any number of years, eventually, you just sort of start to intuitively figure it out. There's a roadmap that emerges slowly over time. Now, even that roadmap probably isn't as good as you think it is. If you really tested your assumptions, you'd find that some of them were flawed and analytics could have helped you be a lot more efficient even then. Rob Collie (00:34:14): But regardless, the perception is that we've got a groove, right? And then when the world completely changes overnight, all of your roadmaps, your travel roadmaps, none of them are valid anymore. And now, you need a replacement and you need it fast. And so, what happens is, is that analytics spending, BI spending, whatever you want to call it, or activity, actually increases during times of crisis. So, you got a healthy baseline business. It's an industry that's not withering and dying in good times, but it actually it's like a hedge against bad times. Rob Collie (00:34:47): When I saw that research years and years ago, when I was working at Microsoft Corporate, we just come out of the dot-com crack up, we'd seen that BI spending it across the IT industry was the only sector that went up during that time where everything else was falling. It's like, "Oh, okay." So, not only do I enjoy this stuff, but I really should never get out of it. It's like one of the best future proofing career moves you can make is the work in this field. And so, I mean, we've seen it, right? The early days of the COVID crisis, you're right when no one knew the range of possible outcomes going forward was incredibly wide. The low end and the high end were exponentially different from one another. Rob Collie (00:35:29): And so, we experienced in our business, sort of a gap in spring and early summer last year. We weren't really seeing a whole lot of new clients, people who are willing to forge a brand new relationship. Again, what happens when a crisis hits? You slam on the brakes. No unnecessary spending first of all. Let's get all the spending under control, because we don't know as a company what's going to happen in the industry, right? You see a lot of vendor spending freezes and of course, to other companies, we're a vendor, right? So, our existing clients, though, doubled down on how much they used us and how much they needed us. Rob Collie (00:36:08): And then later in the year, the new client business returned, and we actually ended up, our business was up last year, despite that Q2 interruption and sort of making new friends. And this year, holy cow like whatever was bottled up last year is coming back big time. And so, yeah. You never really want to be the ghoul that sort of morbidly goes, "Oh, crisis." From a business perspective, yeah, anything that changes, anything that disrupts the status quo tends to lead to an increased focus on the things that we do. Greg Beaumont (00:36:43): Yeah, I think something you said there, too, was when you don't know what's going to happen was when the business intelligence spending increased. I mean, the intelligence and business intelligence, it's not just a slogan. The purpose of these tools is to find out the things you don't know. So when there's uncertainty, that's when BI can provide that catalyst to sort of add some clarity to what you're actually dealing with. Rob Collie (00:37:06): Yeah, I've been using, even though I'm not a pilot, I've never learned to fly a plane or anything. I've been using an aviation metaphor lately, which is windshield is nice and clear. You might not be looking at the instruments on your cockpit very much, right? You know there's not a mountain in front of you, you can see how far away the ground is. And you could sort of intuit your way along, right? But then suddenly, whoosh clouds. And oh, boy, now, you really need those instruments, right? You need the dashboards, you need the altimeter, you need the radar. You need all that stuff so much more. Rob Collie (00:37:37): And so, and our business has kind of always been this. The reason I've been using this metaphor is really for us, it's like given how fast we operate, and I think you can appreciate this having come from a Microsoft partner consulting firm before Microsoft years ago, our business model, we move so fast with projects. We're not on that old model with the original budget and the change orders and all of that. That was all dysfunctional. Rob Collie (00:38:01): It was necessary, because of the way software worked back then, but it was absolutely dysfunctional. It's not the way that you get customer satisfaction. So, we've committed to the high velocity model. But that means seeing the future of our business financially two months in the future is very difficult relative to the old sort of glacial pace, right? If there's a mountain there, we're going to have months to turn around it. Krissy Dyess (00:38:26): To add a bit to your analogy there, Rob. I am married to a pilot and I have gone up in the small tiny airplane. And before the gadgets, there's actually the map. The paper map, right? So, you had the paper map, which my husband now would hand to me. And he'd tell me, "Okay, let me know the elevations of different areas to make sure we're high enough, we're not going to crash into the mountains." Krissy Dyess (00:38:47): What's happened is people just they got used to different ways that they were doing things. They were forced into these more modern ways. And I think even now, this wave of seeing this catalyst we can change and how are other people changing is also driving the people to seek help from others in terms of getting guidance, right? Because even though you've had the change, it doesn't necessarily mean that the changes that you made were 100% the right way and you can learn so much from others in the community and the people that are willing to help. Krissy Dyess (00:39:24): And I think that's one of the things too, that our company provides as a partner, we're able to kind of go alongside. We've seen what's works, what doesn't work, what are some of those pitfalls? What are those mountains approaching? And we're really able to help guide others that want to learn and become better. Rob Collie (00:39:42): Yeah. I mean, this is us getting just a little bit commercial, but you can forgive us, right? That high velocity model also exposes us to a much larger denominator. We see a lot at this business that accumulates. The example I've given before is and this is just a really specific techy, so much of this is qualitative, but there's a quantitative. It's sort of like a hard example of like, "Oh, yeah, that's right. This pattern that we need here for this food spoilage inventory problem is exactly the same as this tax accounting problem we solved over there, right?" As soon as you realize that you don't need to do all the figuring out development work, you just skip to the end. Rob Collie (00:40:22): And really, most of the stuff that Krissy was talking about, I think, is actually it's more of the softer stuff. It's more of the soft wisdom that accumulates over the course of exposure to so many different industries and so many different projects. That's actually really one of the reasons why people come to work here is they want that enrichment. Greg Beaumont (00:40:38): Yeah, that makes sense. Because you see all these different industries and you actually get exposed to customers that are the best in the business for that type of, whether it be a solution or whether it be a product or whether it be like a framework for doing analytics or something like that. So, you get that exposure and you also get to contribute. Rob Collie (00:40:55): Even just speaking for myself, in the early days of this business, when it was really still just me, I got exposure to so many business leaders. Business and IT leaders that, especially given the profile of the people who would take the risk back in 2013, you had to be some kind of exceptional to be leaning into this technology with your own personal and professional reputation eight years ago, right? It was brand new. So, imagine the profile of the people I was getting exposed to, right? Wow, I learned so much from those people in terms of leadership, in terms of business. They were learning data stuff from me, but at the same time, I was taking notes. Greg Beaumont (00:41:33): Everybody was reading your blog, too. I can't count the number of times I included a reference to one of your articles to help answer some questions. And it was the first time I was introduced to the Switch True DAX statement. And then I'd print that. Rob Collie (00:41:47): Which- Greg Beaumont (00:41:48): Sent that link to many people. "Don't do if statements, do this. Just read this article." Rob Collie (00:41:53): And even that was something that I'd saw someone else doing. And I was like, "Oh, my God, what is that?" My head exploded like, "Oh." Yeah, those were interesting days. I think on the Chandu podcast, I talked about how I was writing about this stuff almost violently, couldn't help it. It was just like so fast. Two articles a week. I was doing two a week for years. There was so much to talk about, so many new discoveries. It was just kind of pouring out in a way. Krissy Dyess (00:42:24): Greg, you came in to the role around 2016. And to me 2017 was really that big year with the monthly releases where Power BI just became this phenomenon, right? It just kept getting better and better in terms of capabilities and even the last couple years, all the attention around security has been huge, especially with the health and life science space. And last year, with this catalyst to shift mindsets into other patterns, working patterns using technology, do you feel like you've seen any kind of significant shifts just compared to last year or this year? Greg Beaumont (00:43:05): Yeah. And so something that burns my ears every time I hear it is when people call Power BI a data visualization tool. It does that and it does a great job. Rob Collie (00:43:11): I hate that. Greg Beaumont (00:43:12): But it's become much more than that. When it launched, it was a data visualization tool. But if you think about it at that time, they said, "Well, business users can't understand complex data models, so you have to do that in analysis services." Then they kind of ingested analysis services into Power BI and made it more of a SaaS product where you can scale it. There's Dataflows, the ETL tool, which is within Power BI, which is an iteration of Power Query, which has been around since the Excel days. So, now you have ETL. You have effectively from the old SQL Server world, you have the SSIS layer, you have the SSAS layer. With paginated reports, you have the SSRS layer. And you have all these different layers of the solution now within an easy to use SaaS product. Greg Beaumont (00:43:55): So this evolution has been happening, where it's gobbling up these other products that used to be something that only central IT could do. And now, we're putting that power by making it easier to use in the hands of those analysts who really know what they want from the data. Because if you think about it, the old process was is you go and you give the IT team your requirements, and they interpret how to take what you want, and translate it into computer code. Greg Beaumont (00:44:21): But now, we're giving those analysts the ability to take their requirements and go do it themselves. And there's still a very valid place for central IT because there's so many other things they can do, but it frees up their time to work on higher valued projects and I see that continuing with Power BI, right? But like we're adding AI, ML capabilities and data volumes keep increasing then capabilities I think will continue to expand it. Rob Collie (00:44:46): Greg, I used to really caused a storm when I would go to a conference that was full of BI professionals. And I would say that something like, "What percentage of the time of BI project, traditional BI project was actually spent typing the right code?" The code that stuck, right? And I would make the claim that it was less than 1%. So, it's like less than 1% of the time of a project, right? And everyone would just get so upset at me, right? But I just didn't understand why it was controversial. Rob Collie (00:45:19): Like you describe like yeah, we have these long requirements meetings in the old model. Interminably long, exhausting, and we'd write everything down. We'd come up with this gigantic requirements document that was flawed from the get-go. It was just so painful. It's like the communication cost was everything and the iteration and discovery, there wasn't enough time for that. And when I say that the new way of building these projects is sometimes literally 100 times faster than the old way. Like it sounds like hyperbole. Greg Beaumont (00:45:53): It's not. Yeah. Rob Collie (00:45:54): It can be that fast, but you're better off telling people, it's twice as fast because they'll believe you. If you tell them the truth, they'd go, "Nah, you're a snake oil salesman. Get out of here." Greg Beaumont (00:46:07): Yeah. And I think the speed of being able to develop, too, it's going to basically allow these tools to be able to do things that people didn't even dream of in the past. It's not just going to be traditional business use cases. I know in healthcare, something that's a hot topic is genomics, right? Genomics is incredibly complex then you go beyond Power BI and into Azure at that point, too and Cloud compute and things like that. Greg Beaumont (00:46:31): So, with Genomics, you think about your DNA, right? Your DNA is basically a long strand of computer code. It is base pairs of nucleic acids, adenine, thymine, and guanine, cytosine that effectively form ones and zeros in a really long string. Rob Collie (00:46:46): Did you know it effortlessly he named those base pairs? There's that biology background peeking back out. Greg Beaumont (00:46:52): I did have to go look it up before the meeting. I said, "Just in case this comes up, I need to make sure I pronounce them right," so. Rob Collie (00:46:59): Well, for those of us who listen to podcasts at 1.5x speed, that is going to sound super impressive, that string there. Greg Beaumont (00:47:05): Yeah. I should call out, too, though that I'm not a genomics expert, so some of what I'm saying here, I'm paraphrasing and repeating from people I've talked to who are experts, including physicians and researchers. So, this long string of code, if you sequence your entire genome, the file is about 100 gigabytes for one person, okay? At 100 gigabytes, you can consume that, but if you want to start comparing hundreds of people and thousands of people in different patient cohorts, all of a sudden, it gets to be a lot of information and it gets very complex. Greg Beaumont (00:47:35): If you think of that strand of DNA as being like a book with just two letters that alternate, there's going to be paragraphs and chapters and things like that, which do different things. So, one of the physicians I spoke to worked with Children's Cancer. Here's kind of where the use case comes in. So, you take something breast cancer where there's BRCA1 or BRCA2, BRCA1, BRCA2 genes where if you have it, there's a measurable increased probability that you'll get that type of cancer within a certain age range. There's a lot of other diseases and cancers, where it might be 30 genes. And depending on different combinations of those genes, it changes the risk of getting that specific type of cancer. Greg Beaumont (00:48:17): But this physician told me that there are specific children's cancers, where they know that if they have certain combinations of genes, that they have a very high probability of getting this cancer. And when the child actually feel sick and goes to the doctor, it's already spread and it's too late. So, if you can do this sequencing, basically run it through machine learning algorithms, so it will determine the probability, you could effectively catch it at stage zero. Because these cancers, it's something that could be related to growth hormones and as you're growing up, and as you become an adult, you're then no longer at risk of getting that childhood cancer. So, if they could identify it early and treated at stage zero, instead of stage 4, it sounds sci-fi, but the tools are there to do it. Greg Beaumont (00:49:01): It just never ceases to amaze me that you watch the news and they talk about self-driving cars and identifying when a banana is ripe, and things like that. But it's like, you know what? These same tools could be out there changing people's lives and making a measurable difference in the world. I think just especially post COVID, I'll expect to see a lot more investment in these areas. And also, interest because I think that might be one of the positives that comes out of this whole experience. Rob Collie (00:49:27): I do think that the sort of the worlds of Medicine and Computer Science are on a merging course. Let's not call it collision course. That sounds more dramatic. There is a merging going on. You're right DNA is biologically encoded instructions by an RNA. The mRNA vaccine is essentially injecting the source code that your body then compiles into antibodies. It's crazy and it's new. There's no two ways about it. Rob Collie (00:49:56): mRNA therapies, in general, which of course they were working on originally as anticancer and sort of just like, "Oh, well, we could use it for this, too." And there's all kinds of other things too, right? Gosh, when you go one level up from DNA or some point of abstraction, you get into protein folding. And whoa, is that... Greg Beaumont (00:50:15): Crazy, yeah. Rob Collie (00:50:16): ... computationally. We're all just waiting for quantum computers, I think. Greg Beaumont (00:50:20): Now, I'll have to call out that I'm making a joke here, so people don't take me seriously. But if you think about it, the nucleus in each of your cells contains an important model of that DNA, right? There isn't just a central repository that everything communicates with. You have a cache of that DNA in every cell in your body, except red blood cells, which perform a specific task. There may be more of the power automated the human body. But cheap attempt at a joke there, so. Rob Collie (00:50:44): Well, I like it, I like it. Let's go in with both feet. I've also read that one of the reasons why it's difficult to clone adult animals is because you start off with your original DNA, but then you're actually making firmware updates to certain sections of the DNA throughout your life. And so, those edits that are being made all the time are inappropriate for an embryo. Greg Beaumont (00:51:09): Yep. Rob Collie (00:51:10): And so, if you clone, you create an embryo, right? And now, it's got these weird adult things going on in it. That's why things kind of tend to go sideways. It can all come back to this notion of biological code and it's fascinating. A little terrifying, too, when you start to think of it that way. I've listened to some very scary podcasts about the potential for do-it-yourself bioweapon development. There was this explosion back, in what, in the '90s when the virus and worm writers discovered VVA. Remember that? We call them the script kiddies that would author these viruses that would spread throughout the computer systems of the world. And a lot of them, the people writing these things were not very sophisticated. They weren't world renowned hackers. Greg Beaumont (00:51:53): For every instance where you can use this technology to cure cancer, you're right that there's also the possibility of the Island of Dr. Moreau, right? You go look up CRISPR Technology, C-R-I-S-P-R, where they can start splicing together things from different places and making it viable. And 10 years ago, they had sheep that were producing spider webs in their milk and it's just, there's crazy stuff out there if you kind of dive into the dark depths of Biology. Now that we went down the rabbit hole, how do we correct course, right? Rob Collie (00:52:23): Well, we did go down a rabbit hole, but who cares? That's what we do. Greg Beaumont (00:52:26): Even you kind of step it back up to just kind of easy use cases in healthcare, so one of the ones that we use as a demo a lot came from a customer, and this was pre-COVID. But something as simple as hand washing, you don't think about it much. But when you're in the hospital, how many of those people are washing their hands appropriately when they care for you. And there's some white papers out there, which are showing that basically, there are measurable amounts of infections that happen in hospitals due to people not washing their hands appropriately. So, a lot of healthcare organizations will anonymously kind of observe people periodically to see who's doing a good job of washing their hands. Rob Collie (00:53:04): I was going to ask, how is this data collected? Greg Beaumont (00:53:06): This customer actually had nurses who were using a clipboard and they would write down their notes, fax it somewhere, and then somebody would enter it into Excel. So, there was this long process. And with another TS, who covers teams, we basically put a PLC together in a couple days, where they enter the information into a power app within teams, so they made their observation, entered it in. It did a write back straight to an Azure SQL Database at that time. Now, they might use the data verse. And then from Azure SQL DB, you can immediately report on it and Power BI. It even set up alerts, so that if somebody wasn't doing a good job, you could kind of take care of the situation, rather than wait for two days for the Excel report to get emailed out, and maybe lower the infection rates in the hospital. Greg Beaumont (00:53:53): So, it saved time from the workers who are writing things down and faxing things just from a sheer productivity perspective. But it also hopefully, I don't know if it will be measurable or not, but you'd have some anticipated increase in quality, because you're able to address issues faster. And that's the simplest thing ever, right? You can spend a billion dollars to come up with a new drug or you can just make sure are people washing their hands. Rob Collie (00:54:17): Both data collection and enforcement, they happen to be probably the same thing. There's like, "Oh, I'm being watched." The anonymity is gone. That's a fascinating story. Okay. What kinds of solutions are you seeing these days? What's happening out in the world that you think is worth talking to the audience about? Greg Beaumont (00:54:38): We're seeing this ability to execute better where the tools are easier to use, you can do things faster, but there's still challenges that I see frequently out there. So, I know something that you all are experts in its data modeling and understanding how to take a business problem and translate it into something that's going to perform well. So, not only do you get the logic right, but when somebody pushes a button they don't have to go to lunch and come back, they get a result quickly. That's still a challenge. And it's a challenge, because it's not always easy, right? I mean, it's the reason cubes were created in the first place was because when you have complex logic and you're going against a relational database, the query has to happen somewhere, but like that logic. Greg Beaumont (00:55:19): So take for example, if somebody wants to look at year over year percent change for a metric and they want to be able to slice it by department, maybe by disease group, maybe by weekend versus weekday, and then they want to see that trend over time. If you translate that into a SQL query, it gets really gnarly really fast. And that problem is still real. One of the trends I'm seeing in the industry is there's a big push to do everything in DirectQuery mode, because then you can kind of manage access, manage security, do all of those necessary security things in one place and have it exist in one place. Greg Beaumont (00:56:00): But when you're sending giant gnarly SQL queries back to relational databases, even if they're PDWs with multiple nodes, it gets very expensive from a compute perspective, and kind of when you scale out to large number of users, concurrency is still an issue. So that's something where you look at recently what Power BI has come out with aggregations and composite models. That's some of the technology that I think can mitigate some of those problems. And even if we think about something like Azure synapse, right? You can have your dedicated SQL pools then you can have a materialized view. A materialized view is effectively a cache of data within synapse, but then you can also have your caches in Power BI, and kind of layer everything together in a way that's going to take that logic and distribute it. Greg Beaumont (00:56:46): Does that make sense? Rob Collie (00:56:47): It does. I think this is still a current joke. The majority of cases where we've encountered people who think they want or need DirectQuery, the majority of them are actually perfect poster children case studies for when you should use cash and import mode. Right? It turns out the perceived need for DirectQuery, there is a real percentage of problems out there for which DirectQuery is the appropriate solution and it is the best solution. But it's the number of times people use it is a multiple of that real ideal number. Rob Collie (00:57:17): I think part of it is just familiarity. Still, I've long talked about how we're still experiencing as an industry the hangover from most data professionals being storage professionals. Everyone needed a database, just to make the wheels go round. The first use of data isn't BI. The first use of data is line of business applications. Every line of business application needed a database, right? So, we have minted millions of database professionals. this is also why I think partly why Power BI gets sort of erroneously pigeonholed as a visualization tools, because people are used to that. They're used to, we have a storage layer and reports layer, that's it, right? Rob Collie (00:57:56): Reporting services was Microsoft's runaway successful product in this space. Paginated reports is still around for good reason. And I think that if you're a long-term professional in this space with a long history, even if you're relatively young in the industry, but you've been working with other platforms, this storage layer plus visuals layer is just burned in your brain. And this idea of this like, "Why do you need to import the data? Why do you need a schedule? Why do you need all this stuff?" It's like as soon as people hear that they can skip it, and go to DirectQuery, they just run to

In this talk from the Azure Data Fest held on June 25, 2021, Jon Tupitza explores automated ML in the Azure ML service.

Join Seth as he welcomes Shivani Santosh Sambare to talk about Prebuilt Docker Images for Inference in Azure Machine Learning. Jump to:[00:17] Show begins[00:29] Welcome Shivani[00:38] What are the challenges working with ML environments?[01:11] Solutions to ML challenges/environments = Prebuilt Docker Images for Inference[02:12] How do I make this work with other specialized environments?[04:10] Demo: Deploying PyTorch model using Azure ML[04:30] Scoring script [06:50] End demo & recap[09:06] Learn more Learn more:Concept Prebuilt Docker Images: https://aka.ms/AIShow/PrebuiltDockerImages/AzureML/Doc Python Extensibility Solution: https://aka.ms/AIShow/PrebuiltDockerImages/PythonExtSolution Blog post: https://aka.ms/AIShow/PrebuiltDockerImages/TechComm/Blog Zero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezAI Show Playlist https://aka.ms/AIShowPlaylistDon't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribe

Join Seth as he welcomes Shivani Santosh Sambare to talk about Prebuilt Docker Images for Inference in Azure Machine Learning. Jump to:[00:17] Show begins[00:29] Welcome Shivani[00:38] What are the challenges working with ML environments?[01:11] Solutions to ML challenges/environments = Prebuilt Docker Images for Inference[02:12] How do I make this work with other specialized environments?[04:10] Demo: Deploying PyTorch model using Azure ML[04:30] Scoring script [06:50] End demo & recap[09:06] Learn more Learn more:Concept Prebuilt Docker Images: https://aka.ms/AIShow/PrebuiltDockerImages/AzureML/Doc Python Extensibility Solution: https://aka.ms/AIShow/PrebuiltDockerImages/PythonExtSolution Blog post: https://aka.ms/AIShow/PrebuiltDockerImages/TechComm/Blog Zero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezAI Show Playlist https://aka.ms/AIShowPlaylistDon't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribe

In Episode #49 of the SAP on Azure video podcast we talk about yet another blog post about the new SAP Private Link service for Azure, Sending Notifications from BTP applications to the SAP Fiori Launchpad, deploying SAP IQ NLS HA solutions using Azure shared disks on Windows server, link collection for SAP on Azure, upcoming session about ABAP, Past Present and Future and updates of the famous Azure Charts. Then we have Bart Delanghe & Thijs Zandvliet showing us an SAP Microhack about CashFLow Predictions using Cosmos DB, Synapse, Azure Machine Learning & Power BI that you can easily build yourself following the detailed tutorial on GitHub. https://github.com/hobru/SAPonAzure

On this episode from our #MSBuild recap, Sethu Raman is here to highlight how Managed Endpoints provides out-of-box capabilities to operationalize models. Sethu will demo how to deploy and score a GPU model: highlighting dev experience with CLI & local deployment and how to perform safe rollout: highlighting metrics, log analytics integration.Jump to:[00:16] AI Show welcomes welcomes Sethu[00:38] How Managed Online Endpoints supports Azure Machine Learning[02:20] Intro to Endpoint concepts and deployments[03:16] Demo 1 - Deploy and score a GPU model[07:40] Demo 2 - Perform safe rollout[11:11] Deployment slots with different modelsLearn more:How do deploy Managed Online Endpoints: https://aka.ms/AIShow/HowToDeploy/ManagedEndpointsAI Show Live: https://aka.ms/AIShowCreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeGet Started with Machine Learning https://aka.ms/AIShow/StartMLAI for Developers https://aka.ms/AIShow/AIforDevelopersAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezFollow Sethu https://twitter.com/Sethu20Don't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribeJoin us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

On this episode from our #MSBuild recap, Sethu Raman is here to highlight how Managed Endpoints provides out-of-box capabilities to operationalize models. Sethu will demo how to deploy and score a GPU model: highlighting dev experience with CLI & local deployment and how to perform safe rollout: highlighting metrics, log analytics integration.Jump to:[00:16] AI Show welcomes welcomes Sethu[00:38] How Managed Online Endpoints supports Azure Machine Learning[02:20] Intro to Endpoint concepts and deployments[03:16] Demo 1 - Deploy and score a GPU model[07:40] Demo 2 - Perform safe rollout[11:11] Deployment slots with different modelsLearn more:How do deploy Managed Online Endpoints: https://aka.ms/AIShow/HowToDeploy/ManagedEndpointsAI Show Live: https://aka.ms/AIShowCreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeGet Started with Machine Learning https://aka.ms/AIShow/StartMLAI for Developers https://aka.ms/AIShow/AIforDevelopersAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezFollow Sethu https://twitter.com/Sethu20Don't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribeJoin us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

John Savill, Azure Cloud Solution Architect and YouTube personality for his very popular series of Azure training videos, has a lot to say about customers using Azure and how you can keep up with all of the latest innovations in the Cloud. Media file: https://azpodcast.blob.core.windows.net/episodes/Episode374.mp3 YouTube: Episode 374 - John Savill on Staying Current with Azure - YouTube Resources: John Savill - YouTube   Other updates: Empowering operators on their cloud migration journeyhttps://azure.microsoft.com/en-us/blog/empowering-operators-on-their-cloud-migration-journey/AzureFunBytes Episode – Intro to Azure Machine Learning with Henk Boelman!https://devblogs.microsoft.com/devops/azurefunbytes-episode-intro-to-azure-machine-learning-with-henk-boelman/Public preview: Azure Machine Learning VS Code Integrationhttps://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-vs-code-remote?tabs=extension Open source API Portal now generally availablehttps://azure.microsoft.com/en-us/updates/apiportal/ Microsoft announces plans to establish first datacenter region in Malaysiahttps://azure.microsoft.com/en-us/updates/microsoft-announces-plans-to-establish-first-datacenter-region-in-malaysia/  

Leveraging Kubernetes ​and Azure Arc to train ML Models ​across hybrid cloud. In this episode Seth talks with Saurya Das about this new Azure Machine Learning integration with Azure Arc – to enable Data Scientists to use existing kubernetes infrastructure on-premises or in multicloud to run machine learning. Jump to:[00:48] What is Machine Learning?[01:28] Run Azure Machine Learning anywhere[02:37] What is Azure Arc?[03:24] Demo: Use on premises Azure Machine Learning clusters using Azure Arc Learn more:Azure Machine Learning Blog - https://aka.ms/AIShow/AzureMLBlogAzure Arc Blog - https://aka.ms/AIShow/AzureArcBlog AI Show https://aka.ms/AIShowCreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeGet Started with Machine Learning https://aka.ms/AIShow/StartMLAI for Developers https://aka.ms/AIShow/AIforDevelopersAzure Machine Learning https://aka.ms/AIShow/AzureML Follow Seth https://twitter.com/sethjuarezDon't miss new episodes, subscribe to the AI Show https://aka.ms/aishowsubscribe Join us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

Leveraging Kubernetes ​and Azure Arc to train ML Models ​across hybrid cloud. In this episode Seth talks with Saurya Das about this new Azure Machine Learning integration with Azure Arc – to enable Data Scientists to use existing kubernetes infrastructure on-premises or in multicloud to run machine learning. Jump to:[00:48] What is Machine Learning?[01:28] Run Azure Machine Learning anywhere[02:37] What is Azure Arc?[03:24] Demo: Use on premises Azure Machine Learning clusters using Azure Arc Learn more:Azure Machine Learning Blog - https://aka.ms/AIShow/AzureMLBlogAzure Arc Blog - https://aka.ms/AIShow/AzureArcBlog AI Show https://aka.ms/AIShowCreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeGet Started with Machine Learning https://aka.ms/AIShow/StartMLAI for Developers https://aka.ms/AIShow/AIforDevelopersAzure Machine Learning https://aka.ms/AIShow/AzureML Follow Seth https://twitter.com/sethjuarezDon't miss new episodes, subscribe to the AI Show https://aka.ms/aishowsubscribe Join us every other Friday, for an AI Show livestream on Learn TV and YouTube https://aka.ms/LearnTV - https://aka.ms/AIShowLive

We got a chance to chat with Saurya Das, Senior Program Manager at Microsoft, who is working on extending Azure Machine Learning capabilities to Kubernetes clusters whether they are on-prem or in the cloud with the help of Azure Arc as the centralized control plane.  Media File: https://azpodcast.blob.core.windows.net/episodes/Episode371.mp3 YouTube Video Resources: Run Azure Machine Learning anywhere - on hybrid and in multi-cloud with Azure Arc - Microsoft Tech Community Hybrid Cloud Machine Learning on Kubernetes with Azure Arc – The New Stack Updates: Microsoft Power Fx: The open-source low-code programming language is in public preview General availability: Azure Monitor for Windows Virtual Desktop New solutions for Oracle WebLogic on Azure Virtual Machines Microsoft named a Leader in Forrester Wave: Function-as-a-Service Platforms Improve supply chain resiliency, traceability, and predictability with blockchain Gartner Announces Supply Chain Winners of the 2021 Power of the Profession Awards    

Machine learning is an important piece of any Analytics solution. The integration between Synapse and Azure ML promotes a seamless collaboration between data professionals in Synapse and ML professionals in Azure ML. In Synapse Studio, users of all skill levels can leverage ML models built in Azure Machine Learning across their organizations to analyze and enrich data. ​Synapse users can also leverage code free and code experiences to seamlessly train ML models using in Synapse notebooks thanks to automated machine learning powered by Azure Machine Learning. ​The integration between Synapse and Azure ML is making data enrichment through machine learning more accessible to Synapse users, which allows for greater analytics insights. Jump To:[00:46] – What is Azure Synapse?[07:08] – Overview of new ML experiences[11:30] – Demo: Leverage Azure Cognitive Services in Azure Synapse[15:21] – Demo: Train a model with AutoML in Azure Synapse[18:06] – Demo: Model scoring in Azure Synapse dedicated SQL PoolFor more information:Getting Started ToolkitAzure Synapse Automated ML TutorialLink an Azure Synapse WS to Azure ML WSMachine Learning in SynapseScoring TutorialCreate a Free account (Azure)Deep Learning vs. Machine Learning Get Started with Machine LearningDon't miss new episodes, subscribe to the AI Show

In this episode of The IoT Unicorn Podcast, Remco Ploeg of Altius discusses the challenges and opportunities of creating carbon neutral homes powered by Azure. Download Transcript Here Episode: 00:00 Pete:  Welcome to the IoT unicorn podcast. This is Pete Bernard from Microsoft. And this podcast is for anyone interested in the long-term technology trends in the IoT space and the journey from here to there. So let's get started. Thank you, Remco, I appreciate your time. Thanks for joining us here. So you're actually based in the Netherlands, and I'm here in Bellevue, Washington, and through the magic of... I'm actually using the Squadcast platform right now to connect and record this, but... Welcome to the IoT Unicorn.   00:16 Remco: Thank you, Pete, for having me.   00:18 Pete: Great, so you're based in the Netherlands, and I've been there a bunch of times. I used to go there actually, when I would go to Barcelona for NWC, there was always like a flight at the crack of dawn from Barcelona, and I would transfer in the Netherlands to get back to Seattle. And then one year, I kinda got smart and I said, you know, I'm gonna go to Amsterdam the night before, get a good night's sleep, and then I'll take the 10 AM Direct to Seattle. So I've spent a bunch of those kind of layover nights in Amsterdam, so it's an awesome, awesome place, but... Are you from there originally?   00:52 Remco: Yes, I'm from Rotterdam, so that's the other big city in the Netherlands so that's south of Amsterdam.   00:58 Pete: I see.   01:00 Remco: And I was a lot I think also on the same plane as yourself, so I had to do a lot... To Seattle with the direct flight in the morning. So... And also coming back with the flight early in the morning in Amsterdam again.   01:12 Pete: Yeah, yeah.   01:14 Remco: So I'm based at the moment in Utrecht, in the middle of the country.   01:18 Pete: Okay. So you've been at Altius for about a year or two, a couple of years?   01:25 Remco: Yeah, a little bit, a little bit more than a year. And the beginning of this year, there was an acquisition of Altius by Avanade...   01:34 Pete: Yes.   01:34 Remco: So, I'm joining formally Avanade from the first of January, the coming year 2021. But already, I think for 6 months, I'm working side by side with my Avanade colleagues.   01:45 Pete: And so I know you've been... I know another thing, I did a little research is you were... Altius was named Microsoft's AI Partner of the Year, so that's a big deal. So tell me more about that. What is Altius in AI? What is the... Do you consider Altius an AI company or more of an IoT company that's using AI or how would you describe it?   02:08 Remco: Yeah, so if you look at Altius, we are at the moment, with 400 people in the UK, Netherlands, and India. We've got a full focus on data and AI, so that's also our focus. So IoT is more or less, no side-job but we saw that...   02:21 Pete: A means to an end.   02:23 Remco: A means to an end, exactly. And then started when I joined Altius so that's one and a half years ago with also combining AI with IoT, 'cause I think that that's a great combination that we have there.   02:36 Pete: Yeah, for sure. A lot of times that we've had folks from Qualcomm and other... More telecom-related, I think we had BT on here recently, and it was like the 5G plus AI plus IoT or pick your network that certainly becomes kind of a game-changer for what you can do with a little bit of data, over a large number of sensors or a lot of data... [02:58] ____.   02:58 Remco: I think, already at the moment, even without 5G, 5G is of course already rolling out, we can already do a lot with IoT.   03:11 Pete: Yeah, so actually interesting on that topic. You talk about IoT, so how much do you think with AI and IoT are you seeing on the Cloud versus the Edge, and how much experience are you getting now, are you seeing in a more of an emergence of Edge AI in addition to the cloud AI or what are you seeing there?   03:31 Remco: Yeah, that's a good question. I don't know, 10 years ago, we were moving everything to the clouds, but now we see some of our clouds going back again, so I do a lot of projects around connected buildings. I think that's a great example with Edge computing, is the amount of sensors, especially in new buildings and smart buildings, it's so enormous that moving every data point to the cloud, it's sometimes technical, not possible, and the second, it's too expensive.   04:00 Pete: Right.   04:01 Remco: So we see there are movement back putting Edge devices in the building itself again, and also doing AI on the Edge device itself because of course what you don't want to do in a building is controlling lights in the cloud, for example, you want to control locally, if the internet connection is out there you want to still put on your lights on. And since a couple of years, we are also doing AI on the Edge and AI on the cloud, of course already a little bit longer, and we are controlling, for example, Edge Tech Systems with AI depending on the expected usage of the building and certain conditions. We put certain settings into the building when running those... Yeah, AI is now on Edge device that help us and our clients a lot to control those devices.   04:54 Pete: Yeah, also I had understood talking to some other customers too about Edge AI, especially in AI Vision, it's something where you want to actually process locally, just from a privacy perspective too. I mean there's a transport, obviously, you can't keep streams of data going up into Azure and doing live video analytics, I guess you could and some people do, but for a lot of maybe smaller implementations or other implementations, you wanna kinda do things locally, act locally and then keep the data on-prem basically, right?   05:24 Remco: Yeah, exactly. So we felt also again the smart, to smart build solutions, things like security with cameras, where we can analyze the data off the camera, to see, okay, is somebody trying to breach into the building or do something else. And we all use Edge AI for it.   05:42 Pete: Right, yeah, yeah, that's fantastic. Tell me a little bit, I heard about some of the carbon-neutral housing efforts that you were doing, so tell me more about that. That sounds intriguing.   05:54 Remco: Yeah. So for one of our clients in the Netherlands, it's a company called TBI, and it's a local company with around six or seven [06:02] ____, and one of their main goals is to be the most sustainable builder in the Netherlands, and for that they are building carbon-neutral houses. So that means that the houses are totally carbon-neutral from a building perspective, but also from a usage perspective. So the people that are going to live in that home, on normal conditions, don't need to pay any energy bill every year, so they are really zero... We call it zero-net houses in the Netherlands but carbon-neutral is a better naming for that.   06:35 Pete: So, they're generating power on-site as well, so they're generating their own power...   06:39 Remco: Exactly.   06:39 Pete: And then also all the building is smart enough and efficient enough where it's only consuming the power that's generated on-site. Is that basically?   06:47 Remco: Exactly, so they put solar panels on it to be extracted data from the solar panels. We've got all kind of meters in the home, smart meters, smart edge meters, all kind of temperature, humidity, CO2 sensors in the home. We extract all the data into the clouds, to do the analytics and to prove that the house is also carbon neutral.   07:07 Pete: And so do those exist? [chuckle] It seems like... That's a tough one, isn't it? I guess it depends on the size of your house and what you're doing in your house, but is that feasible?   07:17 Remco: Yeah. So in the Netherlands, we don't have big houses like in the US.   07:20 Pete: Right, right. No McMansions.   07:23 Remco: So it's more, yeah, so it's more [07:26] ____ houses. And yeah, we have around, also normal houses, and I think 120, 130 meters, square meters, so that's not huge.   07:36 Pete: No, no. The square footage of the house also limits maybe the amount of energy you can create, right? You've got limited space for solar panels.   07:41 Remco: Exactly, exactly. Yeah, yeah. Yeah, exactly. Yeah, yeah, yeah. And one of the other things that is of course very important is, of course, the energy with the solar panels. Second of all, of course, if in the winter, for example, if the homeowner put the window open, the whole day, that house will never be carbon neutral because heating will...   08:05 Pete: Yeah.   08:05 Remco: Go the whole day and all night.   08:07 Pete: Can't solve for bad behaviour.   08:08 Pete: And that's why we need to...   [laughter]   08:10 Remco: Exactly, yeah. So we also try to help the homeowner to get insight in that usage and that energy.   08:18 Pete: I see.   08:18 Remco: To reduce energy.   08:20 Pete: Is the power generated primarily through solar or is there any kind of wind or other geothermal or...   08:25 Remco: No, at the moment, in the houses, it's really solar panels and of course the extra energy that they bring into the home is from green energy. So in the Netherlands, most of the homes use green energy.   08:37 Pete: Ah, see, I see.   08:39 Remco: Or other solar panels...   08:39 Pete: So they're getting a power feed from the grid, from the government grid, that's green energy, that's probably wind-powered, right? And then they're augmenting that with local solar, and then the energy they're consuming...   08:50 Remco: Exactly.   08:50 Pete: Is kind of net neutral, I guess.   08:53 Remco: Net neutral, yeah. Exactly.   08:55 Pete: Fantastic.   08:56 Remco: Yeah. Because in the winter, of course, you don't have enough sun. We don't have enough sun in the Netherlands.   09:00 Pete: Right. Same here, I mean you know...   09:01 Remco: At the end of winter [09:02] ____.   09:02 Pete: Yeah, Northwest, we don't have... There's no sun here.   [laughter]   09:06 Remco: No. A lot of rain.   09:08 Pete: Interesting, wow. So has that solution been deployed then? I mean, your solution with...   09:13 Remco: Yeah. We have now around, I think 700 houses deployed.   09:18 Pete: Wow, fantastic.   09:19 Remco: And depending of the speed, of course, of building extra houses will be added every month. Something like that.   09:25 Pete: So basically what you're doing is you're instrumenting. 'Cause I know in the US and maybe in the Netherlands too, there are ways to instrument your patch panel, your incoming electrical panel, to look at loads on a per circuit basis. And then what you're doing is then you're doing that kind of analysis, you're doing the HVAC kind of heating cooling analysis. Are you doing anything about the appliances themselves in the house? Are there appliances that are kind of determining being smart about their energy usage or is that all just happening sort of asynchronously from the rest of the system?   09:58 Remco: Yeah, so we put some, let's call them Smart Energy power adaptors in the home... To see, okay, the refrigerator, how much energy is that costing, etcetera, etcetera. So we're also getting that data. In the future, there are plans to also put in a small panel in the home to give direct feedback to the... To the homeowner itself. So at the moment, it's more or less... It's more information behind for the homeowners, so they can watch later on how the house is performing. TBI can see it directly, but the homeowner sees it later. So they want to bring that information already directly in the home, so the homeowner can react on it directly.   10:44 Pete: Right, right. So you're measuring current draw from different outlets, for lack of a better term, right? It's... Giving them a heads-up and...   10:52 Remco: Yeah.   10:53 Pete: But in the future, then hopefully these things that are plugged into the wall will get smarter and smarter about... Everyone needs to sort of get a message to sort of go into a low power mode because somebody's running the dryer maybe... I don't know. Like is there intra-appliance communication going on here, or is it just sort of every appliance for themselves?   11:14 Remco: Yeah, we see that already with load balancing. So in the Netherlands, we've got a big amount of electric cars that we are charging at home. And so we've got a lot of solutions also in this case with load balancing. So depending on the usage of the home, the car will load more or less energy. So I expect much more solutions also in the more smaller devices that can do more or less the load balancing and talking to each other what's happening.   11:47 Pete: Yeah, exactly. I think that's kind of key. We actually just installed a level two charger here in the US, 'cause we have a Chevy Bolt, our newest car. It's great, and you know the 110-volt charge here, that doesn't really... That only works for emergencies. It takes like an hour to get five miles of charge, so the level two we had installed and run off a 40-amp breaker in the panel and... Yeah, I could see that the inter sort of communication between devices, that will be sort of maybe the next step.   12:16 Remco: Yeah, we see that already here sometimes with more [12:19] ____ systems that try to connect all the kind of devices to each other. But that's still, in my opinion, more for tech guys like us, because it doesn't work always, and... You need some pack of expertise.   12:33 Pete: And a bunch of logos on the box, it's supposed to work with the other thing, it never does. So, yeah, been there. [laughter]   12:38 Remco: Exactly, yeah. It never does.   12:39 Pete: So what are you using Azure for in this case? I'm curious. What are your... There's obvious things, I can name them, but I'm just curious. How are you leveraging Azure in this particular solution?   12:50 Remco: Yeah. So in the start, we've built our own device, our hardware partner did that in the home. And the device is managed by the Azure IT app, where we get the data from the device into that. But we can also control the device now. So it's...   13:05 Pete: And is that sort of a monitoring device that's kind of like a power monitoring?   13:09 Remco: No, it's more, let's call it a gateway. I think that's the best naming convention for this device. So it's really the center of the device in the home. It'll extract the data from the solar panels, from the heating, from...   13:22 Pete: Okay. Right, right, right.   13:23 Remco: Etcetera, etcetera. And the data is directly feed into the systems. Of course, we've got also some devices that we cannot connect in the home itself, and we extract the data from APIs of those suppliers.   13:36 Pete: Right, right.   13:37 Remco: Those are more or less the two options for data ingestion into the platform.   13:40 Pete: Okay.   13:43 Remco: We're using Azure Digital Twins version two to make a replication of the home itself, so we get data from Outerdesk. Outerdesk is a piece of software where they design the homes with, the data from the Outerdesk we import into Outerdesk... Into the... Sorry, Azure Digital Twins. And we combine that data with the sensor data in the home. And that combination reflects into a digital twin of every home of TBI.   14:10 Pete: Wow. That's cool.   14:10 Remco: And next the data flow into the digital twin, we analyze the data with applications like Timeshare with Insights, where we can do simple Timeshare risk analytics. And of course, this data is all time-based data, solar data, panel data, with consumption and data, and energy data, etcetera, etcetera, so that they can do the fast analytics by themselves. And the other one is we use Azure Stream Analytics, where we can analyze the data for anomaly detection. So we know, for example, one of the biggest dealings with TBIS, it's a really simple one but it's water pressure. So in older homes in the Netherlands, we've got gas boilers, or sometimes electric boilers, and they need a certain water pressure in them. If you don't have enough water pressure, you cannot shower, and you don't have heat. And it's an easy solution because you just put extra water into the boiler system and it works.   15:15 Pete: Right.   15:16 Remco: But yeah, if you are in the morning, and you want to go to the shower and it doesn't work, most people will call, in distress, TBI... And say, "Okay, my boiler doesn't work." So then the mechanic will go to the house and fix the problem, and it gets quite expensive.   15:30 Pete: Right, they're very expensive. So you have... So there's sensors for water pressure in the pipes? Is that...   15:36 Remco: Yeah, no, it's a sensor for water pressure in the boiler.   15:41 Pete: Down in the boiler. Okay.   15:44 Remco: So it's indirect in the pipe of course. And that data we get in, and we see a certain pattern that it's declining every time, and then, of course, we can... Or call the homeowner at first, so they're now calling the homeowner, and ask them "Can you fill it by yourself?" And if not, they will come to you and fill it for you of course. But yeah, it's more... Let's say proactive maintenance. Instead of predictive maintenance, it's more proactive.   16:07 Pete: Right.   16:09 Remco: So that's one of the options they are doing. We're using Azure Machine Learning, also how to calculate optimized boiler temperature, to reduce energy in your boiler systems you can set a certain boiler temperature. And of course, how high the boiler temperature, or how more energy you will consume and you need to find per home the optimized boiler temperature, so we use machine learning for that, Azure Machine Learning. And of course, we use Power BI to present all the data to the stakeholders of TBI.   16:44 Pete: Wow, that's cool. So you're getting your money's worth then, on Azure. [chuckle]   16:48 Remco: Yeah, sorry, yeah.   16:50 Pete: That's cool. Are you doing any Edge AI, speaking of Edge AI, on the gateway itself, or is it really more of a data collector sender?   16:58 Remco: Yeah, so at the moment, it's really in Data Collector and of course we can send commons back. Based on this platform, we are also building out now also for the same customer a connected buildings platform, so same architecture but different use case, and therefore we use Edge devices of course.   17:16 Pete: Yeah, I can imagine. Actually, I had someone from RXR realty on the show about a month ago, I don't know if you've heard that one, but that was interesting 'cause they're focused more on the commercial... They're one of the largest real estate companies in New York City. And so they focus on commercial real estate, and in fact, they're using Azure for a lot of work, safe at work scenarios around social distancing, and mask-wearing, and occupancy, and other things. So I can imagine once you move into a commercial space, there's obviously the energy usage and the efficiency, which you guys are focusing on here for the personal home, but then there's all these other scenarios, and when you get into smart buildings, obviously that's kind of a whole lot more complex.   18:01 Remco: Yep. Yeah, and the other issue where, of course, we checked if we could do something with Edge devices, especially from a machinery perspective in the future that can be in a good solution, but if you look at the moment for the pricing model, between Edge device and then [18:18] ____ device, it's more or less almost 50% cheaper to put you and then, yeah, [18:23] ____ more stupid device in the home.   18:24 Pete: Yeah, no, that's true. That's true.   18:27 Remco: Yeah, and it's getting better also. On the simple devices, you can also already do some simple machine learning or smart analytics stuff, as a Microsoft... They put also a lot of energy, and with Edge [18:43] ____, for example, that can do really simple machine learning on a really simple device... You have a lot of CPU power.   18:52 Pete: So if your had your wish list of, "I really wish this technology existed to help me with these solutions, and it doesn't exist yet," is there anything that's kind of top of mind for you that you could snap your fingers and say, "Kinda wish we had that."   19:05 Remco: That is a really good question. Yeah, so for this use case is more or less for the device in the home. So as TBI, to get a bit of hardware power, maybe build some... Piece of hardware. And it's of course school, and I like that, but if you... As a construction company, do you want also to be in a hardware builder of those devices in the home? So I'm looking forward also, if you look at Microsoft, and what they are doing with things like Surface Laptops, etcetera, will they come ever with a cheap, really good more and less Edge or [19:47] ____ Autos device, Microsoft branded working really good... That's one of the things that...   19:53 Pete: I see. So sort of a Microsoft Edge AI sort of platform or something that...   20:01 Remco: And of course, yeah, there are some... Yeah, so there are, of course, already some development kits for that... With the fusion Kit and, etcetera, but that is more or less for the, yeah... For playing around with AI... It's really cool device. But from a production perspective, you need of course something else.   20:18 Pete: Interesting. Yeah, no, that would be good. That would be good. I think there's a lot of... Just a lot of work ahead of us in terms of... When you talked about Edge AI and just a lot of the things that Altius is doing is certainly on the cutting edge. You said you have about 700 homes, so obviously lots more to go with that. Do you know of any other overseas... I'm wondering if there's any equivalent sort of efforts going on in the US. I know that there's a lot around efficiency, but not necessarily around marrying efficiency with the kind of intelligence... I don't know if there's anything else.   20:52 Remco: No, we see in Europe a good interest now in this solution. We also try to more resell this solution. They say it's Avanade's. And of course, you can use the same concept in a building. I think in the US also, to save energy in a big building, you can save a lot of money.   21:14 Pete: Sure, sure.   21:15 Remco: I think these guys can also help with that.   21:20 Pete: Yeah, yeah, for sure. Fantastic. How's the pandemic been affecting your business over the past... I guess... Year, almost, God forbid.   21:30 Remco: Yeah. I have to say, in the Netherlands, it's quite good. So they are still building houses. There's a big need for houses in the Netherlands, especially cheaper houses. So we see a lot of attention, and I think if you look at the pandemic and data and AI, I think companies need more and more data and AI also during pandemic and also afterwards. From that perspective, I see a good future around this topic. If you look at IoT, a lot of companies are now investing... Okay, how can I do the same without the people, and IoT, of course, can help with that, with more automatic... Think about connect the factories.   22:16 Pete: I think actually it's interesting...   22:18 Remco: [22:19] ____ etcetera, etcetera.   22:19 Pete: One of the things I've heard in a constant theme is the pandemic, obviously, it's been pretty horrific, but the... We are getting a lot more data-savvy as a population, we're learning to understand data, and the importance of data and data can mean life and death in many cases, so just the sort of data savviness of the population, it's a positive thing and like you said, I think people are trying to just now use technology to be safer, to be more efficient, remote and automated. That's kind of fast-forwarded a lot of investment in technology over the past nine months or so. And I guess part of it is doing more with less, in many cases. We're trying to be more efficient and more effective. I think once we can get the pandemic under control, we get the... Climate change comes back into the front page, as you would say, and so the work that you guys are doing in terms of really being smart with energy, energy is such a finite resource and... Although I guess it's infinite, if you consider like the sun and the solar system, but anyway...   23:28 Remco: And the wind.   23:29 Pete: And the wind. I guess it's infinite. No. But that's kind of the next thing is we need to get smart and take some of the technology investments we've made and really apply them into our everyday life and... Yeah, yeah, 20, 30 years from now, this will just be commonplace, not having this kind of smarts in your home, in your building and not having a renewable energy, not being carbon-neutral itself will probably seem very odd a decade from now.   23:58 Remco: If you look at the IoT... IoT is already... They're frightened about... 20 or 25 years...   24:03 Pete: Yes, yes. We had that discussion the other day. I've been involved in it for that long. We used to call it embedded systems, and now it's called IoT. Now, but actually now they're gonna change it, it's gonna be called Edge, so Edge computing is now the cool... Even cooler than IoT. [chuckle] Get ready for another name change, but... Yeah, no, it's fascinating. Well, it's great, I really appreciate you taking the time and explaining what you guys are doing. Any kind of closing thoughts or other things you wanna communicate out to the audience around where this stuff is heading.   24:40 Remco: Yeah, I think what you just mentioned, if you look at sustainability of the epidemic, I think that should be one of the main topics for us in the world, and I think IoT can really help with that, creating that achievement with sustainability. And of course, in your home, it's all small what you're doing, but if everybody's doing it, it's really big for the world.   25:03 Pete: Right, right.   25:05 Remco: So we should use these kind of technologies in our homes, and our buildings. That will really help a lot saving energy and reducing a lot of, yeah, bad air, in the air, in the world.   25:18 Remco: Yeah, you know, I hear you. I think there's a phrase, I think it's like, "I can't solve the problems of the world by myself but I can solve the problems that are here locally, on the ground that I stand". And I guess if everyone is doing that and you kind of using technology in the right way to be sustainable, then it does add up. That's gonna be an important one. Perfect. Great, well, Remco, I really appreciate the time. It's been nice meeting you and I appreciate all the support of the Microsoft community, and hopefully our paths will physically cross at some point, maybe some future Mobile World Congress, I don't know, Barcelona or Netherlands or something in the future.   25:58 Remco: Or maybe on the airport of Amsterdam, man.   26:00 Pete: Yeah, I'm sure. I'm sure we've actually probably passed each other in the airport at some point.   26:03 Remco: Exactly, yeah.   26:04 Pete: Cool, alright. Appreciate...   26:05 Remco: Nice to meet you, Pete.   26:06 Pete: Thanks, Remco. Take care.   26:07 Remco: Thank you.   26:07 Pete: Bye-bye.

Let us show you what powers an end-to-end Retail analytics solution built with Azure IoT.The whole end-to-end solution is available for you to try out on GitHub: https://aka.ms/iotshow/retailFrom detecting that a shelf is empty using IoT Edge, to fulfilling a customer's order, managing stocks, and getting AI-based recommendation for reordering items, the solution is taking advantage of IoT Central, Power apps, Logic Apps, Azure Machine Learning and more.

Let us show you what powers an end-to-end Retail analytics solution built with Azure IoT.The whole end-to-end solution is available for you to try out on GitHub: https://aka.ms/iotshow/retailFrom detecting that a shelf is empty using IoT Edge, to fulfilling a customer's order, managing stocks, and getting AI-based recommendation for reordering items, the solution is taking advantage of IoT Central, Power apps, Logic Apps, Azure Machine Learning and more.

In this video, you will see how you can use drag-and-drop Designer to build, test and deploy a production-ready image classification model with state-of-the-art algorithms.Jump To: [01:19] Demo StartLearn More: Designer OverviewDesigner TutorialDesigner SamplesCreate a Free account (Azure)Deep Learning vs. Machine Learning Get Started with Machine LearningDon't miss new episodes, subscribe to the AI Show

Learn how Azure ML supports Open Source ML Frameworks and MLflow in AzureML. We'll walk through a ScikitLearn and Pytorch example to show the built in support for ML frameworks. We'll also go over how you can take these examples and easily track your artifacts with MLflow.Learn More: Azure ML ExamplesAzure ML Curated EnvironmentsTrack and Monitor ML Flow Create a Free account (Azure)Deep Learning vs. Machine Learning Get Started with Machine Learning Don't miss new episodes, subscribe to the AI Show

In this episode Andrew and Danny run a simple demo that builds a Machine Learning Model using Azure Machine Learning and Python. This is to demonstrate that the tools and the code to build machine learning models are easy to use and accessible...what you need to focus on is the data and choosing the correct model.

Parallel run in Azure Machine Learning enables big data processing in distributed manner. In this episode, you will find out more about Azure Machine Learning dataset; how it can help manage your data in machine learning workflow; and how to use dataset in parallel run to accelerate your big data processing. [02:00] Demo StartLearn More: Azure Machine Learning Datasets Parallel Run Sample NotebookCreate a Free account (Azure) Deep Learning vs. Machine Learning Get Started with Machine Learning Don't miss new episodes, subscribe to the AI Show

When working with a large corpus of data, we often start with an intuition on the latent insights. Azure Cognitive Search has all the tools needed for you to start with just that intuition to build an active learning pipeline. In this session, you will learn how to apply this active learning pattern to the entity extraction task. Knowing a few entities contained in the corpus is all you need to very quickly build a multi-pass enrichment pipeline. The pattern walks you through generating training data to train a model, and integrates the model trained in Azure Machine Learning back into your enrichment pipeline as a new Azure Machine Learning skill.The AI Show's Favorite links:Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure)

Chris Lauren the Principal Program Manager for the Azure Machine Learning Platform goes over the new Azure Machine Learning service. Chris shows you what capabilities data scientists can get across the machine learning lifecycle within a familiar notebook experience. And you'll see how you can use the newly introduced Automated Machine Learning capabilities in Azure ML to build machine learning models in a fraction of the time.

Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines. This enables you to run your machine learning models with data from multiple sources (85+ data connectors supported in ADF). This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.[0:02:18] DemoAzure Data Factory overviewAzure Data Factory docsAzure Machine Learning overviewAzure Machine Learning docsCreate a free account (Azure)

Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines. This enables you to run your machine learning models with data from multiple sources (85+ data connectors supported in ADF). This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.[0:02:18] DemoAzure Data Factory overviewAzure Data Factory docsAzure Machine Learning overviewAzure Machine Learning docsCreate a free account (Azure)

אנחנו לא באמת יודעים איך אומרים “ביי” באוזבקית (יש בכלל שפה כזאת?). אבל הנה מה שאנחנו כן יודעים: מה גיא עשה באתונה? מה אפשר לעשות עם BIT Data Type, וממה צריך להיזהר? מה זה Azure Machine Learning? מה זה Automated Machine Learning? איזה פדיחות אנשים עושים ביום הראשון בעבודה? מה הפתרון הכי טוב ל-Database Maintenance? איך עושים Restore אוטומטי?

This episode first aired in September, 2018: You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning – now a feature of Azure Machine Learning – that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

# News * Marco und der SuperBowl * Deutsche Post kommt mit WhatsApp Alternative (SimsMe): https://www.mobiflip.de/shortnews/simsme-deutsche-post/ * SAML Token encryption in AAD (public preview): https://techcommunity.microsoft.com/t5/Azure-Active-Directory-Identity/Announcing-the-public-preview-for-SAML-token-encryption-support/ba-p/331867 * File Manager 'für OneDrive': https://www.microsoft.com/store/productId/9P7VBBBC49RB * Teams kommt ins Office Paket * Team Limit von 2.500 auf 5.000 erhöht: https://microsoftteams.uservoice.com/forums/555103-public/suggestions/31930651-increase-user-limit-above-2500 * Microsoft Teams Priority Notification & Message Delegation: https://www.microsoft.com/en-us/microsoft-365/blog/2019/02/07/new-capabilities-in-microsoft-365-empower-healthcare-professionals/ * Das neue OWA kommt für alle: https://twitter.com/OfficeNews/status/1090323720042889216?s=20 * Spotify hat Anchor gekauft: http://www.spiegel.de/wirtschaft/unternehmen/spotify-will-mehr-podcasts-anbieten-und-kauft-produktionsfirmen-a-1251912.html # Teams is complete != finished Microsofts Sicht: https://www.microsoft.com/en-us/microsoft-365/roadmap?filters=Rolling%20Out%2CIn%20Development&searchterms=Teams#owRoadmapMainContent * Rolling out * New File experience * Firstline Worker * Webparts as Tabs (heute braucht es ne Page drum herum) * Connect Site with one click * Dev * Yammer Tab * Broadcast meetings * Teams Info am Ordner wenn channel * ProPlus integration Community Sicht: https://microsoftteams.uservoice.com/forums/555103-public * Private Channels (Working on it) 16k * Group Calaneder in Teams (Working on it) 8k * Compact Mode (Planned) 6k * Multi Team Account (Working on it) 6k * Linux Client (On Backlog) 6k * Better planner Integration (partially done) 6k * Who is online in a channel (working on it) 5k * Archive channel (On the backlog) 5k * Teams uses 600MB and is super slow! (Working on it) 4k +1 * Multi Window (working on it) 3k * Access Email from left panel (under review) 3k # Nick Cave, Yuval Harari und Machine Learning Die Basis-Technologie von Azure AD Identity Protection lässt sich in zwei Punkten erläutern: * Yuval Harari: 21 Lektionen für das 21. Jahrhundert: https://www.ynharari.com/de/ * AI wird Musik kreativer und besser machen, als der Mensch das könnte * https://www.sueddeutsche.de/kultur/nick-cave-songwriting-kuenstliche-intelligenz-1.4314308!amp * Cave: Super Buch, aber, die AI wird nie Musik machen können, die 'menschlich' ist (z.B. 'Smells like Teen Spirit') * Reality Check, wo stehen wir --> Zero Alpha ('Schach'), Alexa & Siri, Microsoft Azure Machine Learning Studio * Getting started azure machine learning: https://techcommunity.microsoft.com/t5/ITOps-Talk-Blog/Step-By-Step-Getting-Started-with-Azure-Machine-Learning/ba-p/331327 * Identity Protection, WD ATP & Office ATP, Smart Lockout # Feedback, Kritik, Lob, Fragen? * Email: podcast@hairlessinthecloud.com * Twitter: @hairlesscloud * Web: www.hairlessinthecloud.com (Links zu allen Podcast Plattformen) * YouTube: https://www.youtube.com/channel/UCZyx8_G8bbB0YsjMLUGE87Q * Coverarts by CARO (mit Hilfe von pixabay.com)

Dr. Henry Jerez, Principal Group Product and Program Manager for Azure* Machine Learning Inferencing and Infrastructure at Microsoft, joins Chip Chat to discuss accelerating AI inference in Microsoft Azure. Dr. Jerez leads the team responsible for creating assets that help data scientists manage their AI models and deployments, both in the cloud and at the edge, and works closely with Intel to deliver the fastest-possible inference performance for Microsoft's customers. At Ignite 2018, Microsoft demoed an Azure Machine Learning model running atop the OpenVINO toolkit and Intel® architecture for highly-performant inference at the edge. This capability will soon be incorporated into Azure Machine Learning. Microsoft additionally announced at Ignite a refreshed public preview of Azure Machine Learning that now provides a unified platform and SDK for data scientists, IT professionals, and developers. For more on Microsoft Azure Machine Learning, please visit http://aka.ms/azureml-docs. Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com. Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation

In this interview from Microsoft Ignite, Dr. Ted Way, Senior Program Manager for Microsoft, stops by to talk about Microsoft Azure* Machine Learning, an end-to-end, enterprise grade data science platform. Microsoft takes a holistic approach to machine learning and artificial intelligence, by developing and deploying complex algorithms as well as accelerating them on hardware. Azure Machine Learning is powered by Project Brainwave, using Intel® FPGAs to deliver real-time AI in the form of image recognition and classification, language understanding, speech to text, and text to speech. Intel FPGAs shine when processing unstructured data and serving a response with very low latency. At Ignite, Microsoft announced four new algorithms – ResNet-152, DenseNet-121, VGG-16, and SSD-VGG – which will allow uses even more flexibility when using the Azure Machine Learning platform. To get started with Azure Machine Learning and Intel FPGAs, visit http://aka.ms/rtai. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com. Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation

Demo-rich show that looks at the evolution of PowerShell as the de facto automation scripting tool across Windows and Linux platforms as presented by the father of PowerShell, Jeffrey Snover. Check out the ability for Visual Studio Code to run PowerShell inside of CloudShell. See how you can leverage the Cloud with PowerShell to protect secrets and harness Azure Machine Learning to advance your management scenarios. Also visit https:microsoft.com/powershell to learn more. Session THR2305 - Filmed Monday, September 24, 18:25 PM EDT at Microsoft Ignite in Orlando, Florida. Subject Matter Expert: Technical Fellow/Chief Architect for Azure Storage and Cloud Edge. Chief Architect for Windows Server. Inventor of PowerShell.

Microsoft CVP Julia White joins Jeremy Chapman to demonstrate Microsoft’s approach to AI across the areas of experiences across the areas of knowledge mining, Cognitive Services and Azure Machine Learning. Watch as Julia walks through practical applications for how you can harness AI for your apps today, including the use of Azure Search and Cognitive Services to analyze the recently released JFK files; training the custom vision service for retail scenarios; and Azure Machine Learning for sales optimization. Learn more and try out these examples for yourself at: www.microsoft.com/ai https:/aka.ms/JFKFiles https:/aka.ms/GoDatabricks Subject Matter Expert: Microsoft Corporate Vice President Julia White, leads product marketing for Microsoft Azure. In this function, Julia is responsible for the value proposition & global go to market strategy, customer engagement, and evangelism.

You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, MA, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning - now a feature of Azure Machine Learning - that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

Neste episódio falamos sobre Machine Learning e como é o dia a dia de quem trabalha neste mercado que vem ganhando destaque nos últimos tempos. Feed do podcast: www.lambda3.com.br/feed/podcast Feed do podcast somente com episódios técnicos: www.lambda3.com.br/feed/podcast-tecnico Feed do podcast somente com episódios não técnicos: www.lambda3.com.br/feed/podcast-nao-tecnico Pauta: O que é Machine Learning Adoção do Machine Learning nas empresas Fluxo do tratamento a dados (coleta da informação, tratamento, modelo, testes) Caixa de ferramenta do profissional de dados Passo a passo do processo de criacao de um modelo Links Citados: https://www.kaggle.com/ UCI Machine Learning Datasets Azure Machine Learning Studio Azure Data Factory Linguagem R Linguagem Python Octave Weka CS229 - Machine Learning Introdução À Mineração de Dados Análise preditiva com Azure Machine Learning e R Participantes: Diego Nogare - @DiegoNogare Lazaro Fernandes Lima Suleiman - @lazarofl Lucas Teles - @lucasteles42 Orlando Gomes - @_orlandomariano Edição: Luppi Arts Créditos das músicas usadas neste programa: Music by Kevin MacLeod (incompetech.com) licensed under Creative Commons: By Attribution 3.0 - creativecommons.org/licenses/by/3.0

This is episode 53 recorded on June 13th 2018… where John & Jason talk about new initiatives in their world, discussed MyAnalytics, Azure Machine Learning, and some of the Power BI Service limits & what to do about them. For show notes please visit www.bifocal.show

It was two years ago today that I had what I refer to now as my “Blues Brothers moment.” If you’re not familiar with the movie, then this GIF below depicts the seen elegantly enough. via GIPHY (https://giphy.com/gifs/blues-brothers-clarity-nBQefMWjqdLc4) As promised, here’s the live stream I did from the hotel after day one. I’d like to think my on camera presence has improved since then. Also here are the links to the two videos I recorded in the Channel 9 Studios: Decoding Brainwaves with Azure Machine Learning (https://channel9.msdn.com/Series/FWTV-on-9/Decoding-Brainwaves-with-Azure-Machine-Learning?term=fwtv%20on%209) Getting Started with the Brainwave Decoding Competition (https://channel9.msdn.com/Series/FWTV-on-9/Getting-Started-in-the-Decoding-Brain-Signals-Competition)

Letošní Build byl plný AI a Microsoft není jedinou technologickou společností, která zahrnuje umělou inteligenci a strojové učení do svých produktů. Co to ale konkrétně znamená? A jak mohou AI začít používat vývojáři? K čemu? A k čemu ne? O tom všem jsme popovídali s naším lokálním specialistou, microsofťákem Michalem Marušanem. Odkazy: - shrnutí AI novinek z MS Build 2018: https://blogs.msdn.microsoft.com/jennifer/2018/05/14/artificial-intelligence-and-machine-learning-news-from-build-2018/ - Cognitive Services Labs: https://labs.cognitive.microsoft.com/en-us/project-gesture - Custom Vision: https://customvision.ai - export modelů: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-your-model - Cognitive Search: https://azure.microsoft.com/en-us/blog/announcing-cognitive-search-azure-search-cognitive-capabilities/ - AML packages: https://aka.ms/aml-packages - Brainwave (FPGA): http://aka.ms/aml-real-time-ai - ONNX modely: https://gallery.azure.ai/models - Azure Machine Learning: https://docs.microsoft.com/en-us/azure/machine-learning/studio/ - Microsoft AI School: https://aischool.microsoft.com/ - Kurz: Introduction to data science: https://www.edx.org/course/introduction-to-data-science - mDevCamp: mdevcamp.eu/ Twittery atd.: - https://twitter.com/deeedx (Martin Š.) - https://twitter.com/madrvojt (Vojta) Děkujeme Worklio a Radkovi za nové logo! Pokud nechcete, aby vám unikla nová epizoda, odebírejte RSS: https://bit.ly/netcz-podcast-rss, sledujte nás na Twitteru: https://twitter.com/dotnetcezet, na Apple Podcasts nebo v jakékoliv podcastové aplikaci. Hudba pochází od Little Glass Men: https://freemusicarchive.org/music/Little_Glass_Men/

Azure Friday visited various Microsoft booths in the Expo Hall at Build 2018 to learn what's new. In this episode, Lara Rubbelke gets an update on Azure Machine Learning from Matt Winkler.For more information, see:Microsoft AI platformMicrosoft AI SchoolMicrosoft AI solutionsCreate a free account (Azure)Follow @sqlgal Follow @AzureFriday

Azure Friday visited various Microsoft booths in the Expo Hall at Build 2018 to learn what's new. In this episode, Lara Rubbelke gets an update on Azure Machine Learning from Matt Winkler.For more information, see:Microsoft AI platformMicrosoft AI SchoolMicrosoft AI solutionsCreate a free account (Azure)Follow @sqlgal Follow @AzureFriday

  Chase Aucoin has officially been in the information technology business for about ten years, but I am going on 25 years of in-depth research into all things programming, math, and technology. I have had the absolute pleasure of working alongside fellow industry experts, developers, scientists, statisticians, and executives across nearly every field, including, Agriculture, Medicine, Finance, Manufacturing, Logistics, Energy, Software as Product, Consulting, and so many more!  Every day I do my best to mentor those around me, provide training, build specialized skills faster, and lead with kindness, patience, and a servant mentality. I have been able to provide my input and expertise to define cutting-edge solutions for the companies I consult with and help them achieve defined business goals. Links mentioned in today's show: http://microservicemanifesto.com/ https://www.linkedin.com/in/chaseaucoin/ https://twitter.com/ChaseAucoin/ YouTube: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A Differentiable Neural Computers Paper: https://deepmind.com/blog/differentiable-neural-computers/ Tensor Flow: https://www.tensorflow.org/ Google’s AI and MI Services * https://ai.google/ * https://cloud.google.com/products/machine-learning/ Azure Machine Learning: * https://azure.microsoft.com/en-us/services/machine-learning-studio/ The Marfan Foundation: * https://www.marfan.org/ * https://www.facebook.com/TheMarfanFoundation/

In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's latest and most exciting innovations in AI development platforms. Last month, Microsoft launched a set of three powerful new capabilities in Azure Machine Learning for advanced developers to exploit big data, GPUs, data wrangling and container-based model deployment. Extended show notes found here. Thanks to our sponsor Springboard.  Check out Springboard's Data Science Career Track Bootcamp.

Mohit Sharma, Program Manager, talks about how Microsoft has implemented predictive analytics built on Azure Machine Learning for the sales team—making the jobs of sellers, sales managers, and sales executives at Microsoft a bit easier. He discusses how our consolidated tools have reduced manual sales processes and provide business insights through Power BI and Cortana Intelligence Suite, resulting in accelerated pipeline management and more accurate sales forecasting.These questions — and more — are discussed during this session:[03:32] Understanding the shifting role of technology.[05:10] Developing forward-looking reports.[06:58] Overview: How can machine learning help sellers?[08:27] Example: How machine learning helps sellers.[10:27] How machine learning helps sellers find contextual resources at time of action.[12:57] Understanding what sales managers and leaders need to do to be successful.[13:40] How machine learning helps sales leaders and sales managers.[16:17] How machine learning is helping to identify product insights.Access additional technical content, discover new and exciting career opportunities in IT, and much more:Microsoft IT ShowcaseMicrosoft IT CareersLinkedInGet the IT Showcase App

Mohit Sharma, Program Manager, talks about how Microsoft has implemented predictive analytics built on Azure Machine Learning for the sales team—making the jobs of sellers, sales managers, and sales executives at Microsoft a bit easier. He discusses how our consolidated tools have reduced manual sales processes and provide business insights through Power BI and Cortana Intelligence Suite, resulting in accelerated pipeline management and more accurate sales forecasting.These questions — and more — are discussed during this session:[03:32] Understanding the shifting role of technology.[05:10] Developing forward-looking reports.[06:58] Overview: How can machine learning help sellers?[08:27] Example: How machine learning helps sellers.[10:27] How machine learning helps sellers find contextual resources at time of action.[12:57] Understanding what sales managers and leaders need to do to be successful.[13:40] How machine learning helps sales leaders and sales managers.[16:17] How machine learning is helping to identify product insights.Access additional technical content, discover new and exciting career opportunities in IT, and much more:Microsoft IT ShowcaseMicrosoft IT CareersLinkedInGet the IT Showcase App

Mohit Sharma, Program Manager, talks about how Microsoft has implemented predictive analytics built on Azure Machine Learning for the sales team—making the jobs of sellers, sales managers, and sales executives at Microsoft a bit easier. He discusses how our consolidated tools have reduced manual sales processes and provide business insights through Power BI and Cortana Intelligence Suite, resulting in accelerated pipeline management and more accurate sales forecasting.These questions — and more — are discussed during this session:[03:32] Understanding the shifting role of technology.[05:10] Developing forward-looking reports.[06:58] Overview: How can machine learning help sellers?[08:27] Example: How machine learning helps sellers.[10:27] How machine learning helps sellers find contextual resources at time of action.[12:57] Understanding what sales managers and leaders need to do to be successful.[13:40] How machine learning helps sales leaders and sales managers.[16:17] How machine learning is helping to identify product insights.Access additional technical content, discover new and exciting career opportunities in IT, and much more:Microsoft IT ShowcaseMicrosoft IT CareersLinkedInGet the IT Showcase App

Frank and Andy speak with a real, live unicorn (data scientist), Brad Llewellyn. Links: Sponsor: Audible.com (http://thedatadrivenbook.com) – Get a free audio book when you sign up for a free trial! Sponsor: Enterprise Data & Analytics (http://entdna.com) Brad Llewellyn: @BreakingBI (https://twitter.com/BreakingBI) Notable Quotes: Movie Quote – Star Wars: A New Hope (http://www.imdb.com/title/tt0076759/) ([1:55]) Bruce Lee (http://www.imdb.com/name/nm0000045/) ([2:20]) Book reference: Unicorns Among Us (https://www.audible.com/pd/Science-Technology/Unicorns-Among-Us-Audiobook/B00NSXNRAG?source_code=PDTGBPD060314004R) ([3:00]) CBIG (http://charbigroup.com/) – the Charlotte BI Group ([6:45]) Azure Machine Learning (https://azure.microsoft.com/en-us/services/machine-learning/) ([7:45]) Is Data Science only for super-nerds? ([12:50]) H2O’s Driverless AI (https://www.h2o.ai/driverless-ai/) ([13:40]) On Automation… ([14:25]) On Predicsis.ai (https://predicsis.ai/) … ([15:12]) On Amazon Machine Learning (https://aws.amazon.com/machine-learning/) … ([15:45]) “Why is the ‘why’ important to you?” ([17:00]) Game reference: The Incredible Machine (https://en.wikipedia.org/wiki/The_Incredible_Machine_(series)) ([17:30]) Statistical terms (https://en.wikipedia.org/wiki/Statistical_hypothesis_testing) ([19:15]) Microsoft SQL Server R Services ([21:12]) Movie reference: The Blues Brothers (http://www.imdb.com/title/tt0080455/) ([22:25]) “Python started around 1991.” (https://en.wikipedia.org/wiki/Python_(programming_language)) – Frank ([24:17]) Video: Trapping a self-driving car (http://nerdist.com/trap-a-self-driving-car/) ([26:20]) Vanilla Ice (http://www.vanillaice.com/) reference ([27:50]) Movie paraphrase: ) (https://www.microsoft.com/en-us/sql-server/sql-server-r-services)

The drone industry is attracting the attention of many people who have innovative ideas for how to use them, and as a result many companies are now focusing on building new applications for them. According to a recent study, by 2030, fifty percent of the market for industrial drones will be focused on agriculture and photography. Unfortunately, there are currently no standardized safety mechanisms in place and these giant flying objects can be subject to hacking or malfunctioning. DroneWorks, located in Chiba, Japan, is currently working to fix that by building alliances with stakeholders to standardize manufactured drones and build an industrial drone flight controller and management system.Join Jerry Nixon as he welcomes the CEO of DroneWorks, Hironobu Imamura as well as Microsoft Technical Evangelists Kosuke Fujimoto and Hiroshi Ota, as they discuss how they worked together on a project to develop a management solution and malfunction prediction system using Azure IoT Hub and Azure Machine Learning.[03:39] Tell us a little about DroneWorks. What do you do?[05:02] What's the difference between industrial vs. commercial drones?[06:18] What are some of the demands of flight controllers and why are they so difficult to make and make well?[07:53] Can you describe what this solution looked like as well as how you used Azure IoT hub?[09:50] When you went into this project, what were your expectations and were they met?[13:20] What kind of scenarios can you see this being used?[17:28] Describe the development experience while using new technology like Azure IoT Edge?[18:53] What plans do you have for the future of DroneWorks?Use the code and architecture from this project on GitHub, get hands on with IoT labs or start to build your own IoT solution on Azure.

The drone industry is attracting the attention of many people who have innovative ideas for how to use them, and as a result many companies are now focusing on building new applications for them. According to a recent study, by 2030, fifty percent of the market for industrial drones will be focused on agriculture and photography. Unfortunately, there are currently no standardized safety mechanisms in place and these giant flying objects can be subject to hacking or malfunctioning. DroneWorks, located in Chiba, Japan, is currently working to fix that by building alliances with stakeholders to standardize manufactured drones and build an industrial drone flight controller and management system.Join Jerry Nixon as he welcomes the CEO of DroneWorks, Hironobu Imamura as well as Microsoft Technical Evangelists Kosuke Fujimoto and Hiroshi Ota, as they discuss how they worked together on a project to develop a management solution and malfunction prediction system using Azure IoT Hub and Azure Machine Learning.[03:39] Tell us a little about DroneWorks. What do you do?[05:02] What's the difference between industrial vs. commercial drones?[06:18] What are some of the demands of flight controllers and why are they so difficult to make and make well?[07:53] Can you describe what this solution looked like as well as how you used Azure IoT hub?[09:50] When you went into this project, what were your expectations and were they met?[13:20] What kind of scenarios can you see this being used?[17:28] Describe the development experience while using new technology like Azure IoT Edge?[18:53] What plans do you have for the future of DroneWorks?Use the code and architecture from this project on GitHub, get hands on with IoT labs or start to build your own IoT solution on Azure.

The drone industry is attracting the attention of many people who have innovative ideas for how to use them, and as a result many companies are now focusing on building new applications for them. According to a recent study, by 2030, fifty percent of the market for industrial drones will be focused on agriculture and photography. Unfortunately, there are currently no standardized safety mechanisms in place and these giant flying objects can be subject to hacking or malfunctioning. DroneWorks, located in Chiba, Japan, is currently working to fix that by building alliances with stakeholders to standardize manufactured drones and build an industrial drone flight controller and management system.Join Jerry Nixon as he welcomes the CEO of DroneWorks, Hironobu Imamura as well as Microsoft Technical Evangelists Kosuke Fujimoto and Hiroshi Ota, as they discuss how they worked together on a project to develop a management solution and malfunction prediction system using Azure IoT Hub and Azure Machine Learning.[03:39] Tell us a little about DroneWorks. What do you do?[05:02] What's the difference between industrial vs. commercial drones?[06:18] What are some of the demands of flight controllers and why are they so difficult to make and make well?[07:53] Can you describe what this solution looked like as well as how you used Azure IoT hub?[09:50] When you went into this project, what were your expectations and were they met?[13:20] What kind of scenarios can you see this being used?[17:28] Describe the development experience while using new technology like Azure IoT Edge?[18:53] What plans do you have for the future of DroneWorks?Use the code and architecture from this project on GitHub, get hands on with IoT labs or start to build your own IoT solution on Azure.

Founded in 2013, Shareablee is the first and only industry-level measurement solution for media companies and brands wanting to understand the impact and effectiveness of their cross-platform social media campaigns. Shareablee captures real-time interactions of more than +850M consumers against its global dictionary of brands, publishers, TV shows, influencers, celebrities and more.Join David Giard as he welcomes CEO & Founder of Shareablee,Tania Yuki, to the show as they discuss how they use Microsoft Azure and Machine Learning to deliver their solution as well as describe their experience with the Microsoft BizSpark program.[0:53] Tell us about Shareablee. What do you do?[3:25] How does this work? What technologies are you using?[4:47] Are you using the cloud and Azure Machine Learning to tackle this?[5:32] What motivated you to start this company? Follow the conversation @bizspark Become a Fan @ facebook.com/bizspark Join our LinkedIn Group Subscribe to our YouTube videos Subscribe to our podcast via iTunes, Windows Phone Marketplace or RSSIf you're interested in learning more about the products or solutions discussed in this episode, click on any of the below links for free, in-depth information:Websites & Blogs: David Giard's BlogLearn more about ShareableeVideos:(Part 1) Women Building the Future – An Interview with Tereza Nemessanyi(Part 2) Women Building the Future – An Interview with Bernadine Brocker, CEO of Vastari(Part 3) Women Building the Future – An Interview with Diana Paredes, CEO of Suade(Part 4) Women Building the Future – An Interview with Jennifer Whaley, CEO of Pose a Pet(Part 5) Women Building the Future – An Interview with Luan Cox, CEO of Crowdnetic(Part 6) Women Building the Future – See how UAE's startup Nabbesh gives opportunities to women freelancers.

Founded in 2013, Shareablee is the first and only industry-level measurement solution for media companies and brands wanting to understand the impact and effectiveness of their cross-platform social media campaigns. Shareablee captures real-time interactions of more than +850M consumers against its global dictionary of brands, publishers, TV shows, influencers, celebrities and more.Join David Giard as he welcomes CEO & Founder of Shareablee,Tania Yuki, to the show as they discuss how they use Microsoft Azure and Machine Learning to deliver their solution as well as describe their experience with the Microsoft BizSpark program.[0:53] Tell us about Shareablee. What do you do?[3:25] How does this work? What technologies are you using?[4:47] Are you using the cloud and Azure Machine Learning to tackle this?[5:32] What motivated you to start this company? Follow the conversation @bizspark Become a Fan @ facebook.com/bizspark Join our LinkedIn Group Subscribe to our YouTube videos Subscribe to our podcast via iTunes, Windows Phone Marketplace or RSSIf you're interested in learning more about the products or solutions discussed in this episode, click on any of the below links for free, in-depth information:Websites & Blogs: David Giard's BlogLearn more about ShareableeVideos:(Part 1) Women Building the Future – An Interview with Tereza Nemessanyi(Part 2) Women Building the Future – An Interview with Bernadine Brocker, CEO of Vastari(Part 3) Women Building the Future – An Interview with Diana Paredes, CEO of Suade(Part 4) Women Building the Future – An Interview with Jennifer Whaley, CEO of Pose a Pet(Part 5) Women Building the Future – An Interview with Luan Cox, CEO of Crowdnetic(Part 6) Women Building the Future – See how UAE's startup Nabbesh gives opportunities to women freelancers.

Founded in 2013, Shareablee is the first and only industry-level measurement solution for media companies and brands wanting to understand the impact and effectiveness of their cross-platform social media campaigns. Shareablee captures real-time interactions of more than +850M consumers against its global dictionary of brands, publishers, TV shows, influencers, celebrities and more.Join David Giard as he welcomes CEO & Founder of Shareablee,Tania Yuki, to the show as they discuss how they use Microsoft Azure and Machine Learning to deliver their solution as well as describe their experience with the Microsoft BizSpark program.[0:53] Tell us about Shareablee. What do you do?[3:25] How does this work? What technologies are you using?[4:47] Are you using the cloud and Azure Machine Learning to tackle this?[5:32] What motivated you to start this company? Follow the conversation @bizspark Become a Fan @ facebook.com/bizspark Join our LinkedIn Group Subscribe to our YouTube videos Subscribe to our podcast via iTunes, Windows Phone Marketplace or RSSIf you're interested in learning more about the products or solutions discussed in this episode, click on any of the below links for free, in-depth information:Websites & Blogs: David Giard's BlogLearn more about ShareableeVideos:(Part 1) Women Building the Future – An Interview with Tereza Nemessanyi(Part 2) Women Building the Future – An Interview with Bernadine Brocker, CEO of Vastari(Part 3) Women Building the Future – An Interview with Diana Paredes, CEO of Suade(Part 4) Women Building the Future – An Interview with Jennifer Whaley, CEO of Pose a Pet(Part 5) Women Building the Future – An Interview with Luan Cox, CEO of Crowdnetic(Part 6) Women Building the Future – See how UAE's startup Nabbesh gives opportunities to women freelancers.

Can you detect lies with machine learning? Jennifer Marsman can! Carl and Richard chatted with Jennifer while at the NDC conference in Oslo. Jennifer talked about gathering EEG data with Emotiv headsets to do lie detection by taking base line (known true and known false) questions and then applying Azure Machine Learning to classify the data. The conversation dives into the different machine learning techniques available on Azure and how certain algorithms are more effective on different data sets - it turns out EEG data works great with deep neural networking! There are lots of different opportunities in the machine learning space, time to check it out!Support this podcast at — https://redcircle.com/net-rocks/donations

Can you detect lies with machine learning? Jennifer Marsman can! Carl and Richard chatted with Jennifer while at the NDC conference in Oslo. Jennifer talked about gathering EEG data with Emotiv headsets to do lie detection by taking base line (known true and known false) questions and then applying Azure Machine Learning to classify the data. The conversation dives into the different machine learning techniques available on Azure and how certain algorithms are more effective on different data sets - it turns out EEG data works great with deep neural networking! There are lots of different opportunities in the machine learning space, time to check it out!Support this podcast at — https://redcircle.com/net-rocks/donations

In questa puntata parleremo di Azure Machine Learning, una piattaforma di machine learning disponibile sul cloud Azure di Microsoft.Abbiamo chiesto a Davide Mauri di spiegarci di cosa si tratta e di quale vantaggi possiamo avere grazie a questo approccio.

Christina Storm, Director of Design at Microsoft for Cortana Analytics family: including Azure Machine Learning, Data Factory and Stream Analytics, joins Gary Rozanc to discuss what skills are necessary for entry level interactive designers such as being multi-disciplinary and understanding customer development. Christina also shares how important user research and real life scenarios are at the beginning of a project to help students create better designs. Finally, Christina shares what she doesn’t want to see a portfolio!

On this episode of Eat Sleep Code, guest Jennifer Marsman talks about Azure Machine Learning and how Microsoft is bringing AI to the masses.

Join your guide Cory Fowler as he talks to the product teams in Redmond as well as the web community.This week Cory is joined by Craig Kitterman one of the 4 Microsoft Employees behind the Social Media Polling app Straw. Straw takes advantage of a huge amount of the Microsoft Stack including: C#, ASP.NET Web API, Windows Phone, Xamarin (iOS & Android with C#), Azure App Service (Mobile Services, Web Apps [for the marketing site]) and Azure Machine Learning.Show LinksBuilding and running Straw on the Microsoft StackPoll with StrawAndroid AppiOS AppWindows Phone AppWeb ClientShow Poll

Let Supercharged Machine Intelligence Grow Your Company Culture of Big Data Analytics - Part I A few months ago, Azure Machine Learning and Amazon ML were launched. Now, you can simply pay as you go for advanced techniques that facilitate data-driven Predictive Analytics. Just as you were looking more seriously at BI and Big Data everyone wants to sell you Machine Learning. What's going on? Jaap Bloem, Principal Analyst at VINT- the Sogeti Trends Lab sheds some light. twitter.com/BLO2M

Carl and Richard talk to Seth Juarez about the latest developments in the machine learning space for the Microsoft space. The conversation starts out focused on Seth's open source library for doing machine learning in .NET. Seth talks about the challenges of getting your head around machine learning, building models and testing data. Then the discussion turns to the Azure Machine Learning tools, at the moment in preview. This can greatly simplify your experiments with machine learning, providing a huge range of tools for novices and experts a like. It's an exciting time for machine learning, you should check it out!Support this podcast at — https://redcircle.com/net-rocks/donations

Carl and Richard talk to Seth Juarez about the latest developments in the machine learning space for the Microsoft space. The conversation starts out focused on Seth's open source library for doing machine learning in .NET. Seth talks about the challenges of getting your head around machine learning, building models and testing data. Then the discussion turns to the Azure Machine Learning tools, at the moment in preview. This can greatly simplify your experiments with machine learning, providing a huge range of tools for novices and experts a like. It's an exciting time for machine learning, you should check it out!Support this podcast at — https://redcircle.com/net-rocks/donations