Podcasts about datarobot

Artie Intel and Micheline Learning report on Artificial Intelligence for The AI Report. ChatGPT now boasts over 200 million users worldwide. Google DeepMind’s Gemini system is turning heads. It processes and reasons across text, images, audio, and video, outperforming humans on over 30 benchmarks.  Synthesia lets users create AI videos using over 230 avatars in 140 languages.  AI notetakers like Fathom and Nyota are streamlining meetings, while automation tools such as n8n are handling repetitive tasks behind the scenes.  Claude and DeepSeek are making waves for their advanced code generation and reasoning skills, while app builders like Bubble and Bolt empower anyone to create software, no coding degree necessary. Google has launched Gemma 3, a new family of open AI models designed for flexibility and top-tier performance.  DeepSeek, a rising AI star from China, has released DeepSeek-VL, an upgraded model excelling at multimodal reasoning-combining text and image analysis.  OpenAI, has just rolled out the o3-mini model, optimized for efficient reasoning and lower computational costs. Meta is investing a staggering $65 billion in AI this year, including a massive new data center in Louisiana.  Microsoft’s Copilot X Enterprise is transforming productivity in the workplace. Powered by next-gen GPT-4 Turbo, it automates complex tasks across Office 365, integrating text, image, and code in a seamless workflow. Meta’s latest LLaMA 3 model is a powerhouse, boasting over a trillion parameters-fifteen times more than GPT-4.  China’s WuDao 3.0, paired with its new AI supercomputer, is setting records in computer vision, natural language processing, and robotics.  Google DeepMind’s Gemini system is turning heads. It processes and reasons across text, images, audio, and video, outperforming humans on over 30 benchmarks.  Grok-3 from xAI delivers high-performance reasoning, content generation, and deep contextual understanding. AlphaGo, still celebrated for its creative and strategic prowess, has inspired a new generation of AI systems capable of learning, adapting, and even surprising human experts with unconventional solutions. Hisense is unveiling appliances that personalize your environment, boost energy efficiency, and connect seamlessly with your digital ecosystem. DataRobot. DataRobot delivers the industry-leading agentic AI applications and platform that maximize impact and minimize risk for your business. Request A Demo: Datarobot.com/ The AI Report

Joe welcomes Tracy Newell, a seasoned tech leader, mentor, and former Fortune 500 executive, to discuss her new book Hers For the Taking: Your Path to the C-Suite and Beyond. Tracy shares her insights on the challenges and opportunities for women in corporate leadership, drawing from her 30+ years of experience.The conversation kicks off with a look at the current state of gender diversity in the C-suite, where Tracy highlights both progress and the work still to be done. She emphasizes the importance of mentorship, managing through influence, and the power of asking the right questions to advance your career. Tracy also delves into practical strategies for navigating the corporate "jungle gym," overcoming burnout, and balancing professional ambitions with personal priorities.Tune in for an inspiring and empowering discussion that challenges the status quo and redefines what's possible in the world of leadership.TRACEY NEWELL, former president of Informatica and board member, is a renowned business leader. She spearheaded Proofpoint's hypergrowth and held executive roles at Polycom, Juniper Networks, Webex, and Cisco. Recognized as a Top 100 Sales Leader, Tracey serves on multiple boards including Druva, DataRobot, and Sailpoint, and contributes to non-profit organizations.

Pre-order Secrets of the Career Game (out May 13) to access exclusive resources, a private Slack group, and live Q&A sessions with Kendall.

Igor Taber has sat on both sides of the table: first as a VC at Intel Capital, then as an operator at high-growth AI startup DataRobot. Now, as co-founder of Cortical Ventures, he's backing the next generation of AI-first startups. In this episode, Igor shares how his operating experience reshaped his investment lens, what most VCs still miss about early-stage execution, and how founders can break through the AI noise to build something that actually lasts. We also get into: What it really takes to build conviction at the seed stage How Cortical Ventures defines “defensibility” in AI The questions every founder should ask an investor (but usually doesn't) Why your biggest advantage may not be your model — it's your momentum If you're building in AI, fundraising, or trying to turn early traction into a long-term advantage, download this episode. RUNTIME: 39:52 EPISODE BREAKDOWN (1:19) Igor: My investor-operator-investor path was “ a really interesting progression.” (3:33) Previously, a lot of his opinions were rooted in the “VC echo chamber.” (6:56) “ You just try to do your best to be a good partner and be helpful.” (7:43) Why he and co-founder Jeremy Achin started Cortico. (13:39) What makes an early-stage AI team stand out? (17:15) When it comes to solo vs. team, “ we haven't seen any like material difference in outcome.” (18:56) “ I think the biggest company on LinkedIn is called ‘Stealth.'” (22:17) Why TAM isn't a major consideration when evaluating new opportunities. (24:18) His framework for evaluating and validating seed-stage startups. (26:03) The biggest red flags in founder discovery meetings. (28:21) Top mistakes founders make in the first year after closing a seed round. (33:16) How Cortical advises AI founders to build more resilient companies. (35:55) A few things to keep in mind before quitting a secure job to launch a startup. (38:07) How to make yourself more investible in 2025. LINKS Igor Tabor Jeremy Achin Cortical Ventures SUBSCRIBE

Andrew Cleland, Chief Investment Officer at Techstars, shares how the world's leading accelerator invests in early-stage startups. He breaks down what makes a great founder, how Techstars selects startups from tens of thousands of applicants, and why a strong technical differentiator is crucial. Andrew reveals the most common reasons startups get rejected and how Techstars mentors founders to avoid early mistakes. He also talks about the biggest themes shaping the future of venture capital.In this episode, you'll learn:[03:00] How Andrew's background in consulting, startups, and venture capital led him to Techstars [07:34] How Techstars selects startups from thousands of applications—what matters most[14:01] The #1 mistake founders make when applying to Techstars, and how to avoid[20:50] Why founders need to build investor relationships early when thinking about fundraising[26:04] Why the VC industry needs more transparency—and how that benefits foundersThe non-profit organization Andrew is passionate about: Magic BusAbout Andrew ClelandAndrew Cleland is the Chief Investment Officer at Techstars, where he oversees investment strategy, fundraising, and portfolio growth across Techstars' global network of accelerators. With over two decades in venture capital and early-stage investing, he previously led investments at Comcast Ventures and Time Warner Investments. An INSEAD MBA graduate, Andrew has backed dozens of high-growth startups and is focused on empowering the next generation of global founders.About TechstarsTechstars is one of the world's leading startup accelerators, backing thousands of early-stage companies across 50+ accelerator programs worldwide. Since 2006, Techstars has helped launch 20+ unicorns, including SendGrid, DigitalOcean, Uber, Twilio, DataRobot and Outreach. The program provides mentorship, funding, and global networks to help startups scale fast.Subscribe to our podcast and stay tuned for our next episode.

As enterprises across industries seek to leverage artificial intelligence in their workflows, demand is mounting for specialized models designed for specific tasks and verticals, creating new opportunities for companies that manage the entire AI life cycle, such as DataRobot. In this episode of the Tech Disruptors podcast, the company's CEO, Debanjan Saha, joins Bloomberg Intelligence Senior Software Analyst Sunil Rajgopal to discuss the evolving AI-solutions landscape, the importance of predictive analytics and the value of being cloud-agnostic and on-premises-friendly. They also cover DataRobot's revenue model, the future of enterprise apps and the competitive dynamics of the AI space.

Executives from DataRobot, LaunchDarkly and ServiceNow share strategies, actions and recommendations to achieve profitable growth in today's competitive SaaS landscape.Topics Include:Introduction of panelists from DataRobot, LaunchDarkly & ServiceNowServiceNow's journey from service management to workflow orchestration platform.DataRobot's evolution as comprehensive AI platform before AI boom.LaunchDarkly's focus on helping teams decouple release from deploy.Rule of 40: balancing revenue growth and profit margin.ServiceNow exceeding standards with Rule of 50-60 approach.Vertical markets expansion as key strategy for sustainable growth.AWS Marketplace enabling largest-ever deal for ServiceNow.R&D investment effectiveness through experimentation and feature management.Developer efficiency as driver of profitable SaaS growth.Competition through data-driven decisions rather than guesswork.Speed and iteration frequency determining competitive advantage in SaaS.Balancing innovation with early customer adoption for AI products.Product managers should adopt revenue goals and variable compensation.Product-led growth versus sales-led motion: strategies and frictions.Sales-led growth optimized for enterprise; PLG for practitioners.Marketplace-led growth as complementary go-to-market strategy.Customer acquisition cost (CAC) as primary driver of margin erosion.Pricing and packaging philosophy: platform versus consumption models.Value realization must precede pricing and packaging discussions.Good-better-best pricing model used by LaunchDarkly.Security as foundation of trust in software delivery.LaunchDarkly's Guardian Edition for high-risk software release scenarios.Security for regulated industries through public cloud partnerships.GenAI security: benchmarks, tests, and governance to prevent issues.M&A strategy: ServiceNow's 33 acquisitions for features, not revenue.Replatforming acquisitions into core architecture for consistent experience.Balancing technology integration with people aspects during acquisitions.Trends in buying groups: AI budgets and tool consolidation.Implementing revenue goals in product teams for new initiatives.Participants:Prajakta Damle – Head of Product / SVP of Product, DataRobotClaire Vo – Chief Product & Technology Officer, LaunchDarklyAnshuman Didwania – VP/GM, Hyperscalers Business Group, ServiceNowAkshay Patel – Global SaaS Strategist, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Today's guest is perhaps the leading CFO name in data and analytics, the ex-CFO of Tableau and DataRobot. Now as Founder & CEO at Caliper, Fletcher draws on over a decade of executive experience at the intersection of finance and analytics. At Caliper, his mission is transform cloud cost and usage data into actionable insights In this episode Fletcher discusses  The relationship between analytics and the CFO Subscription, net dollar retention, customer retention, and annual recurring revenue as key metrics  Getting from CFO to CEO and the learning curve  How we rely on AI and predictive ML and seasonal patterns to find anomalies Getting to base analytics and starting in AI Challenges and waste with cloud and holding engineers accountable The opportunity to  save 30% on cloud spend Moving from Excel to Google Sheets Connect with Damon LinkedIn: https://www.linkedin.com/in/damon-fletcher-bb8a6614/ https://calipersoftware.ai

Kenny Daniel⁠ is the founder and CEO of ⁠Hyperparam⁠, building tools to make ML dataset curation orders of magnitude more efficient.Look At Your ****ing Data

Not many people make it to VP of Marketing Operations—Kimi Corrigan has done it twice. In this episode, she shares her journey from marketing coordinator to leading marketing ops at companies like Duo Security, Cisco, Wiz, Expel, and DataRobot. We dive into what it really takes to reach VP level, how to position yourself for leadership, and why marketing ops pros should start asking for that title.Kimi also gets real about the challenges of scaling teams, navigating internal politics, and balancing the tactical with the strategic. We talk about how marketing ops has evolved, the role of data and strategy, and why building strong cross-functional relationships is just as important as knowing your way around a tech stack.If you're a marketing ops pro wondering what's next in your career—or how to level up—this one's for you.About Today's Guest With over 19 years in marketing operations and leadership, Kimi Corrigan thrives on optimizing marketing strategies for efficiency and effectiveness.Kimi has held marketing ops leadershiop roles at Duo Security, Cisco, Wiz, Expel, and DataRobot.https://www.linkedin.com/in/kimicorrigan/Key Topics[00:53] - How Kimi got started in marketing operations[03:04] - Early-generation marketing automation[05:54] - How you know if MOPS is working well[10:11] - Navigating people and organizational problems[16:51] - Becoming a VP Marketing Ops[22:31] - Operations and strategy [33:47] - Composable stacks vs. suites[37:51] - MOPS team structure[39:50] - Planning cycleResource Links Learn MoreVisit the RevOps FM Substack for our weekly newsletter: Newsletter

In a video interview with The Forecast, DataRobot CEO Debanjan Saha discusses the need for finding value and building confidence...[…]

In a video interview with The Forecast, DataRobot CEO Debanjan Saha discusses the need for finding value and building confidence...[…]

In this episode, we sit down with Satadru Sengupta and Alexandra Fontova, the visionary leaders behind Dobby, America's most loved home services company. Satadru, Dobby's co-founder and CEO, shares his incredible journey from being an early operator at DataRobot to pioneering AI-driven solutions in the insurance industry. With a career spanning AI, machine learning, and industry-focused go-to-market strategies, Satadru discusses how his passion for technology and ethical innovation drives Dobby's mission to transform home services. Alexandra, co-founder and COO, brings her expertise in customer experience and operational excellence to the conversation. With a background leading high-performing teams at WeWork and a proven track record in growth and customer satisfaction, Alex reveals the strategies that have propelled Dobby to achieve an industry-leading NPS of +80. Host: Jake Aaron Villarreal, leads the top AI Recruitment Firm in Silicon Valley www.matchrelevant.com, uncovering stories of funded startups and goes behinds to scenes to tell their founders journey.  If you are growing AI Startup or have a great story to tell, email us at: jake.villarreal@matchrelevant.com  

A podcast interview with David Cohen, the co-founder and CEO of Techstars, the startup accelerator, which has backed thousands of startups. In the episode, Cohen addresses recent criticism about its corporate culture and discusses startup trends in Europe and beyond in 2024.

In season one, Kimi Corrigan joined us for a snake draft of our dream MOPs teams. Now, we've cornered her to focus on the star of the show. Us. I mean. Her.Get ready for a wild ride as bestie-of-the-show Kimi Corrigan, VP of Marketing Operations at DataRobot, spills all about her latest chaotic weekend, from party-planning headaches to dreaming up future gatherings (with a little less chaos next time). We dive into the good stuff, like the all-too-real struggles of home repairs, wardrobe planning for the new season (fashion is serious business, people!), and the unpredictable life of parenting teenagers.Kimi opens up about her unexpected passion for golf and gives us a peek into her team's quirky work culture—think The Mighty Ducks, but make it corporate. You'll feel like part of the team as she shares the inside jokes, camaraderie, and why they don't just work together, they root for each other, too.Kimi teases some exciting projects on the horizon, like her current obsession with collecting fun, out-there domain names (for that one day they'll come in handy) and her plans to launch a YouTube channel dedicated to Rummikube, where she'll mix strategy with laughs. Get ready for an episode packed with relatable chaos, good vibes, and a peek into Kimi's entrepreneurial brain. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.prettyfunnybusiness.com/subscribe

Hamel Husain is a seasoned AI consultant and engineer with experience at companies like GitHub, DataRobot, and Airbnb. He is a trailblazer in AI development, known for his innovative work in literate programming and AI-assisted development tools. Shawn Wang (aka Swyx) is the host of the Latent Space podcast, the author of the essay 'Rise of the AI Engineer,' and the founder of the AI Engineer World Fair. In this episode, Hamel and Swyx share their unique insights on building effective AI products, the critical importance of evaluations, and their vision for the future of AI engineering.Chapters00:00 - Introduction and recent AI advancements06:14 - The critical role of evals in AI product development15:33 - Common pitfalls in AI product development26:33 - Literate programming: A new paradigm for AI development39:58 - Answer AI and innovative approaches to software development51:56 - Integrating AI with literate programming environments58:47 - The importance of understanding AI prompts01:00:37 - Assessing the current state of AI adoption01:07:10 - Challenges in evaluating AI models--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is an Integrated Development Environment for Large Language Models. It enables product teams to develop LLM-based applications that are reliable and scalable. To find out more go to humanloop.com

Hugo speaks with Dan Becker and Hamel Husain, two veterans in the world of data science, machine learning, and AI education. Collectively, they've worked at Google, DataRobot, Airbnb, Github (where Hamel built out the precursor to copilot and more) and they both currently work as independent LLM and Generative AI consultants. Dan and Hamel recently taught a course on fine-tuning large language models that evolved into a full-fledged conference, attracting over 2,000 participants. This experience gave them unique insights into the current state and future of AI education and application. In this episode, we dive into: * The evolution of their course from fine-tuning to a comprehensive AI conference * The unexpected challenges and insights gained from teaching LLMs to data scientists * The current state of AI tooling and accessibility compared to a decade ago * The role of playful experimentation in driving innovation in the field * Thoughts on the economic impact and ROI of generative AI in various industries * The importance of proper evaluation in machine learning projects * Future predictions for AI education and application in the next five years * We also touch on the challenges of using AI tools effectively, the potential for AI in physical world applications, and the need for a more nuanced understanding of AI capabilities in the workplace. During our conversation, Dan mentions an exciting project he's been working on, which we couldn't showcase live due to technical difficulties. However, I've included a link to a video demonstration in the show notes that you won't want to miss. In this demo, Dan showcases his innovative AI-powered 3D modeling tool that allows users to create 3D printable objects simply by describing them in natural language. LINKS The livestream on YouTube (https://youtube.com/live/hDmnwtjktsc?feature=share) Educational resources from Dan and Hamel's LLM course (https://parlance-labs.com/education/) Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence (https://investors.upwork.com/news-releases/news-release-details/upwork-study-finds-employee-workloads-rising-despite-increased-c) Episode 29: Lessons from a Year of Building with LLMs (Part 1) (https://vanishinggradients.fireside.fm/29) Episode 30: Lessons from a Year of Building with LLMs (Part 2) (https://vanishinggradients.fireside.fm/30) Dan's demo: Creating Physical Products with Generative AI (https://youtu.be/U5J5RUOuMkI?si=_7cYLYOU1iwweQeO) Build Great AI, Dan's boutique consulting firm helping clients be successful with large language models (https://buildgreat.ai/) Parlance Labs, Hamel's Practical consulting that improves your AI (https://parlance-labs.com/) Hamel on Twitter (https://x.com/HamelHusain) Dan on Twitter (https://x.com/dan_s_becker) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne)

In this episode, we speak with the Co-Founder and CEO of Armada, Dan Wright. Armada is the world's first full-stack edge computing platform, revolutionizing connectivity, compute, and AI solutions. Armada enables companies to rapidly deploy, operate and monitor a complete modular data center to the remote corners of the world. Before Armada, Dan was the CEO of DataRobot and COO of AppDynamics. He is invested in the future of technology as an investor, advisor, and board member for various visionary startups, including Abnormal Security, Avi Networks (acquired by VMware), and Embrace. Dan is a Board Member of JDRF. To learn more about this organization click here. I am your host RJ Lumba.  We hope you enjoy the show.  If you like the episode click to follow.

Jepson Taylor is the former Chief AI Strategist at DataRobot and Dataiku. YouTube: https://www.youtube.com/@jakenewfield Spotify: https://open.spotify.com/show/4k9DDGJz02ibpUpervM5EY Apple Podcasts: https://podcasts.apple.com/us/podcast/for-the-sake-of-argument/id1567749546 Twitter: https://twitter.com/JakeNewfield --- Support this podcast: https://podcasters.spotify.com/pod/show/jake-newfield/support

Vijay Reddy, partner at Mayfield, talks about the latest innovations and trends shaping the artificial intelligence (AI) landscape. He shares his venture capital journey and describes Mayfield's unique focus on cognitive plumbing and Cognition-as-a-Service (CaaS), unraveling what these concepts entail. Vijay also offers practical advice for startup founders keen on tapping into the myriad opportunities within the realm of AI.In this episode, you'll learn:[1:24] From a 10th grade circuits and chips enthusiast to technology executive and founder[5:50] Vijay's evolution as a venture capital investor fueled by technology curiosity[13:41] Debate about AI being a good or bad thing - it's not worthy![15:04] Get these 3 things right for success with an AI-first startup.The non-profit organization that Vijay is passionate about: UpwardAbout Vijay ReddyVijay Reddy is a partner at Mayfield with over a decade of AI and deeptech investing experience. Prior, Vijay was an investor at Clear Ventures and Intel Capital. He witnessed AI's rise from its inception, and with a founder-first approach, he has invested across the AI stack, backing companies like SambaNova, DataRobot, and BabbleLabs. He began his career as an entrepreneur, co-founding a startup after leaving his PhD program, and has held senior roles at Broadcom and Intel.About MayfieldMayfield is a Silicon Valley-based venture capital firm that invests in AI-first companiesat the Seed, Series A and Series B stages. The firm has raised 20 U.S. funds since 1969, and currently has $3 billion under management. Mayfield takes pride in an investment team that has a founder DNA and operates from a shared set of beliefs and partners for the long term with entrepreneurs pursuing big ideas. Companies in Mayfield's portfolio include: Outreach, seekout, alchemy, auradine, chemix, crunchbase, Gutsy, LexCheck, OwnID, Qwiet, Vijil, VERSA Networks among others.  Subscribe to our podcast and stay tuned for our next episode.

Dan Wright is no typical founder. With a formidable background at software powerhouses like AppDynamics and DataRobot, Dan has shifted his focus towards bridging the technological gap in edge computing with his latest venture, Armada. As the world's first full-stack edge computing platform, Armada integrates computing and AI capabilities directly where data is generated.Dan started Armada after recognizing a significant shift in data generation to the edge and the inadequate response of centralized clouds to the demands of heavy data producers in sectors like oil and gas and manufacturing.In today's episode, Dan discusses the origin story of Armada and its strategic partnership with Starlink, which allows it to extend its edge computing capabilities to remote locations. We also discuss:The impact of edge computingArmada's product suite and roadmapBridging the digital divideBuilding a business with SpaceXAnd much more…This episode is brought to you by the Italian Trade Agency (ITA). • Chapters •00:00 Intro & ITA Ad01:11 What is Armada and why did you start the company?02:33 Adding hardware capabilities04:34 What is edge computing?07:00 75% of all data will be generated at the edge10:12 Serving remote corners of the world11:22 What kind of efficiency will edge computing be able to make?14:06 Armada x Starlink16:59 How did Armada build their relationship with Starlink?18:51 How did you convince SpaceX to work with you?20:30 Without Starlink, can Armada still be successful?22:11 Armada's product suite25:41 Creating demand for 3rd parties in the marketplace28:11 The Galleon30:44 Customer traction, targeting, and product in the field32:58 Armada's smallest but still relevant customer34:18 The competitive landscape36:13 Extreme testing for Galleon38:45 Capital raising41:38 What keeps Dan up at night43:17 Galleons but in space?44:47 10 year vision46:04 Dan's opinion on Starship's next flight test50:40 What does Dan do for fun? • Show notes •Armada's website — www.armada.aiDan's socials — https://twitter.com/danwrightSFMo's socials — https://twitter.com/itsmoislamPayload's socials — https://twitter.com/payloadspace / https://www.linkedin.com/company/payloadspacePathfinder archive — Watch: https://www.youtube.com/@payloadspace Pathfinder archive — Listen: https://pod.payloadspace.com/episodes • About us •Pathfinder is brought to you by Payload, a modern space media brand built from the ground up for a new age of space exploration and commercialization. We deliver need-to-know news and insights daily to 19,000+ commercial, civil, and military space leaders. Payload is read by decision-makers at every leading new space company, along with c-suite leaders at all of the aerospace & defense primes. We're also read on Capitol Hill, in the Pentagon, and at space agencies around the world.Payload began as a weekly email sent to a few friends and coworkers. Today, we're a team distributed across four time zones and two continents, publishing five media properties across multiple platforms:1) Payload, our flagship daily newsletter, sends M-F @ 9am Eastern2) Pathfinder publishes weekly on Tuesday mornings (pod.payloadspace.com)3) Polaris, our weekly policy briefing, publishes weekly on Tuesdays4) Payload Research, our weekly research and analysis piece,  comes out on Wednesdays You can sign up for all of our publications here: https://payloadspace.com/subscribe/

// Abstract Diego, David, Ads, and Katharine, bring to light the risks, vulnerabilities, and evolving security landscape of machine learning as we venture into the AI-driven future. They underscore the importance of education in managing AI risks and the critical role privacy engineering plays in this narrative. They explore the legal and ethical implications of AI technologies, fostering a vital conversation on the balance between utility and privacy. // Bio Diego Oppenheimer - Moderator Diego Oppenheimer is a serial entrepreneur, product developer and investor with an extensive background in all things data. Currently, he is a Partner at Factory a venture fund specialized in AI investments as well as a co-founder at Guardrails AI. Previously he was an executive vice president at DataRobot, Founder and CEO at Algorithmia (acquired by DataRobot) and shipped some of Microsoft's most used data analysis products including Excel, PowerBI and SQL Server. Diego is active in AI/ML communities as a founding member and strategic advisor for the AI Infrastructure Alliance and MLops.Community and works with leaders to define AI industry standards and best practices. Diego holds a Bachelor's degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University. Ads Dawson A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics. Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection for example. Ads is also a networking veteran and love a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries. Katharine Jarmul Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy. David Haber David has started and grown several technology companies. He developed safety-critical AI in the healthcare space and for autonomous flight. David has educated thousands of people and Fortune 500 companies on the topic of AI. Outside of work, he loves to spend time with his family and enjoys training for the next Ironman. A big thank you to our Premium Sponsors,  @Databricks  and  @baseten  for their generous support! // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

In this curated episode of the Revenue Builders Podcast, John McMahon and John Kaplan, sponsored by Force Management, delve into the intricacies of sales hiring, dissecting the reasons behind sales rep failures, and exploring the critical aspects of assessing the hiring process in a leadership role. Joined by Chris Riley, CEO of Winning Edge Advisors and former president of Worldwide Field Operations at DataRobot, the trio shares insights into the challenges of identifying the wrong hire, the importance of coaching, and the nuances of adapting to evolving business landscapes.KEY TAKEAWAYS[00:00:52] Identifying Sales Rep Failures: Chris Riley discusses the multifaceted reasons behind sales rep failures and emphasizes the role of managers in daily coaching and feedback.[00:01:56] Critical Traits: The hosts and Chris highlight the significance of work ethic, coachability, and the ability to absorb and learn as key traits in successful sales hires.[00:03:33] Character Traits: The discussion touches on the challenge of changing inherent character traits, emphasizing the importance of recognizing a person's fundamental nature.[00:05:10] Skills Transferability: Chris shares insights into evaluating if a salesperson's skills are transferable to different sales environments and the potential pitfalls of misplacing talent.[00:06:34] Strategic Hires: The hosts stress the importance of strategic hires in business ops and go-to-market strategy, pivotal for proper business metrics and effective training.HIGHLIGHT QUOTES[00:03:47] "If they're hungry enough, as you said, they're persistent. You might be able to help them develop skills, but you're not gonna change their character."[00:04:20] "Persistence, heart, and desire. You just have to give it everything you got."[00:06:52] "One, if you hire really good people and then when you onboard them the right way so you can decrease the ramp time and then you train them and develop 'em to increase their productivity and you give 'em to good leaders, you're probably not gonna churn them either."Listen to the full episode with Chris Riley through this link:https://revenue-builders.simplecast.com/episodes/driving-accountability-and-building-trust-with-chris-rileyCheck out John McMahon's book here:Amazon Link: https://a.co/d/1K7DDC4Check out Force Management's Ascender platform here: https://my.ascender.co/Ascender/

On today's episode, serial entrepreneur Diego Oppenheimer shares his unique perspective on AI through the lens of his diverse career experiences. Starting as a mechanical engineer, Diego describes the pivotal moment he decided to shift his focus to data and analytics. After 5+ years shipping multi-billion dollar products at Microsoft, he went on to found and lead MLOps company Algorithmia through its eventual acquisition by DataRobot. Diego's current company, Guardrails AI, is focused on safely aligning ML models with business goals. He is also a Partner at Factory, a venture fund specializing in AI investments.Chet and Diego explore the evolving landscape of AI, the importance of experimentation and guardrails, and how foundational models are revolutionizing human-computer interactions. They also touch on how investors are thinking about AI and the one thing Diego is most looking forward to in the next year.Connect with Diego:https://www.linkedin.com/in/doppenheimer/https://twitter.com/doppenhehttps://github.com/doppenheGuardrails AI:https://www.guardrailsai.com/Factory VC: https://www.linkedin.com/company/factoryhq/ 

Explore the future of collaborative ML workflows in this engaging episode with Dr. Greg Michaelson, Co-Founder of Zerve. Dr. Michaelson introduces the groundbreaking Zerve IDE and Pypelines project, addressing the critical gap in AutoML for commercial use and pinpointing why many A.I. projects don't meet their objectives. Gain insights into steering AI initiatives towards success and enhancing project communication, all in this insightful session. This episode is brought to you by Oracle NetSuite business software (https://netsuite.com/superdata), and by Prophets of AI (https://prophetsofai.com), the leading agency for AI experts. Interested in sponsoring a SuperDataScience Podcast episode? Visit https://passionfroot.me/superdatascience for sponsorship information. In this episode you will learn: • Why Zerve IDE is so sorely needed [04:50] • Pypelines: AutoML open-source in python [30:00] • Why most commercial A.I. projects fail and how to ensure they succeed [47:45] • How AutoML will impact the role of the data scientist [53:21] • Greg's background as a pastor and working at DataRobot [1:03:40] • How to develop impressive communication and storytelling skills [1:16:16] Additional materials: www.superdatascience.com/753

Drop the technical jargon. Instead, speak your audience's language.Language has power. And talking to your audience not as “marketer” but as someone who understands your customer's world is key. This means speaking to their cares, concerns and frustrations. Do this and your audience engagement will soar. In this episode, we're looking at a show that literally speaks its audience's languages: Spanish and English. It's Apple TV's first bilingual comedy, Acapulco, a show that has been recognized by the Imagen Foundation for its meaningful portrayal of latinos in the media. And together with the help of our special guest, Head of Americas Marketing, Cristina Daroca, we talk about showing the outcome first, speaking your audience's language, choosing a visually stunning setting, and more. So grab your sunglasses for this episode of Remarkable.About our guest, Cristina DarocaCristina Daroca is Senior Director of Global Demand and Americas Marketing at Riverbed Technology. She joined Aternity in July of 2020 as Director of Global Demand Generation, and the company merged with Riverbed in December of 2021. She previously served as Senior Manager of Global Marketing Programs for DataRobot. She has also worked at companies like Mighty AI and LevelUp. She was born and raised in Spain, and now lives in Boston.About RiverbedRiverbed transforms data into actionable insights across the entire digital ecosystem and accelerates performance for a seamless digital experience. Riverbed is the only company with the collective richness of telemetry from network to app to end user, that illuminates and then accelerates every interaction, so organizations can deliver a seamless digital experience and drive enterprise performance. Riverbed offers two industry-leading portfolios: Alluvio by Riverbed, a differentiated Unified Observability portfolio that unifies data, insights, and actions across IT, so customers can deliver seamless, secure digital experiences; and Riverbed Acceleration, providing fast, agile, secure acceleration of any app, over any network, to users anywhere. They have thousands of partners, and market-leading customers globally – including 95% of the FORTUNE 100. Riverbed is headquartered in San Francisco, but they have lots of employees in the Boston area because of an acquisition.About AcapulcoAcapulco is a TV show about a 20-something Mexican guy named Maximo who gets the job of his dreams working at a luxury resort in Acapulco. But then he finds out that it's much more complicated than he expected. His new co-workers refuse to show him the ropes, the guests are super demanding, and he finds that it creates challenges at home. The story is told in flashbacks by an older Maximo who has clearly had a successful career, as he's now living in a beautiful seaside house, looking back on his beginnings. The show stars Eugenio Derbez as the mature Maximo Gallardo, and young Maximo is played by Enrique Arrizon. Maximo's best friend, Memo, who he works with at Las Colinas is played by Fernando Carsa. His boss, Don Pablo, is played by Damian Alcazar. And his love interest, Julia, is played by Camila Perez.It premiered in 2021, with two seasons out on Apple TV, and a third on the way. And it's been nominated for the Critics Choice Awards, Hollywood Critics Association Television Awards, Imagen Foundation Awards, and more. It's Apple TV's first Spanish bilingual comedy.What B2B Companies Can Learn From Acapulco:Show the outcome first. Customers want to hear about results. Then you can support those results with details of how you help them get there. It's like how In Acapulco, we meet an older, wealthy Maximo who tells his story in flashbacks of how he became successful. Ian says, “A lot of times, we'll say 10 X ROI, here's your case study. But if we get the story element at the beginning part of it using flashbacks, you can tell a story that's gripping from the moment you dig into it.“ And Cristina adds, “Sometimes I think we get too hung up on, ‘What's the pain that the customer is feeling? What's the problem like?' Let's paint a picture of what the end state looks like for them, and then walk them through, ‘This is how you get there.'” Give your audience a glimpse of their future after they've been using your product to grab their attention.Speak your audience's language. Cristina says, “Especially in B2B, we tend to be very buttoned up and using fancy words. And hey, we're talking to humans. It's so important in marketing to know your customer's language, to use the language they're using to really speak the way they do.” She says that's why Acapulco really resonates with her as a bilingual Spanish and English speaker. Choose a visually stunning setting. 80s Acapulco was a beautiful and evocative place that had cachet as a celebrity vacation spot. This is hugely important, because Ian says, “If you were to tell the same story in Finland in the winter, for example, it would feel extremely different than telling the story in Acapulco in the 80s. It's another piece that we often don't think of setting when we do our marketing stories, because we're in an office. Setting is so important and we don't think about it enough in B2B marketing.”Capitalize on the resources you have. Everyone is working on tight budgets with limited resources. But Cristina says, “We can control what we have and what we can make out of it, and how we can make it a good experience for our customers, for our guests, for the audience that we're serving.” Maximo came from humble beginnings, having been raised by a single mother. But he used what resources he did have to find success. So focus on doing your best work with what you have.Quotes*”It's so important in marketing to know your customer's language. To use the language they're using to really speak the way they do. Especially in B2B. We tend to be very buttoned up and use fancy words. And hey, we're talking to humans. They're also humans. They're talking the same language. So yeah, just really understand your customers, know how they speak and use that same language with them.” - Cristina Daroca*”This is what it is. The economy is what it is. It's all out of our control. We can't really control the budget cuts, the team cuts. What we can control is what we have and what we can make out of it. And how we can make it a good experience for our customers, for our guests, for the audience that we're serving.” - Cristina DarocaTime Stamps[0:55] Meet Cristina Daroca, Head of Americas Marketing at Riverbed[1:50] Why are we talking about Acapulco?[2:19] What does Cristina's work at Riverbed entail?[2:59] What is Acapulco about?[6:05] Why is it important to speak your audience's language?[6:53] About the setting of Acapulco[14:08] What are marketing lessons we can take from Acapulco?[22:01] How does Cristina think about marketing at Riverbed?[24:17] How does Cristina prove the ROI of content?[25:08] What marketing strategy has worked well for Cristina in the past?[26:10] Learn more about Riverbed's roadshow[32:12] What advice would Cristina give to other marketers?LinksWatch AcapulcoConnect with Cristina on LinkedInLearn more about RiverbedAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both non-fiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Senior Producer). Remarkable was produced this week by Meredith Gooderham, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise.

Miguel Armaza sits down with Maëlle Gavet, CEO of Techstars, a premier global accelerator and one of the world's most active pre-seed investors. Techstars was founded in 2006 and has backed almost 4000 companies with a combined portfolio market cap of $105B. Some of their invested companies include Remitly, Chainalysis, DataRobot, and Alloy.We discuss:Trends and lessons learned at Techstars after 16 years, countless programs, and backing 9000 foundersMaelle's criticism of the venture industry and how it could improve to better serve foundersHow Techstars has been able to scale internationally and across programs with a partially decentralized approachA deep dive into Maelle's impressive background as a former founder and operator… and a lot more!Want more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary, instead? Check out the Fintech Leaders newsletter and join 60,000+ readers and listeners worldwide!Miguel Armaza is Co-Founder and General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: bit.ly/3jWIp

Today's guest is Ted Kwartler, Field CTO of Generative AI at DataRobot. DataRobot is an AI-powered software company that helps enterprises automate processes from end-to-end. Ted returns to the platform in conversation with Emerj CEO and Head of Research Daniel Faggella to examine what challenges for software development teams look like from the perspective of leadership. Later, Ted offers actionable insight on handling pushback on goals, assigning team members based on data-verified skills rather than work politics and planning future initiatives. This episode is sponsored by Pieces. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Episode 315 of The VentureFizz Podcast features Jordan Fliegel, Managing Director of Techstars' NYC and Sports accelerators. Athlete, entrepreneur, investor, mentor, are just some of the words to describe Jordan's career. Originally a fixture of the Boston tech scene with his first company, CoachUp, and now a major part of the NYC tech scene as the leader of the Techstars program, it is shocking that Jordan hasn't been a guest on The VentureFizz Podcast, since that is the two markets that we are focused on. Well, the wait is finally over and it was worth the wait! If you are not familiar with Techstars, it is the world's most active pre-seed investor and one of the top accelerators in the world. Since 2006, Techstars has worked with over 9,000 founders from companies like DigitalOcean, Zipline, DataRobot, and more that have gone on to raise over $26B in funding. In this episode of our podcast, we cover: * The similarities between coaching athletes and mentoring entrepreneurs. * Jordan's background story and his experience playing professional basketball in Israel. * The inspiration behind CoachUp, a venture backed marketplace that connects athletes with personal coaches, and how he landed Steph Curry as an investor. * Working closely with his childhood friend, Jeremy Levine, on Draft, a fantasy sports company, which was acquired by Paddy Power Betfair. * Launching the Techstars Sports Accelerator in Indianapolis and portfolio company examples, plus the details on the NYC program. * The inside look at how companies are selected for Techstars. * And so much more.

Steve Pretre, partner at World Innovation Lab (WiL), takes us on an extraordinary journey from his upbringing in Silicon Valley to becoming a key player in the insurtech and venture capital worlds. He shares the thrilling journey of starting Metromile and leading it through the IPO stage, highlighting some of the biggest challenges of starting an insurtech startup. Steve also dispels the skepticism about corporate venture capital firms (CVCs).In this episode, you'll learn:[3:47] Discover invaluable lessons from the pioneers of the insurtech industry[7:40] The story of Metromile: “I was excited and naive enough to think that we could pull that off.” - Steve Pretre[15:43] Early-stage investing isn't just about funds but also about providing strategic support to startups[22:40] Insights into corporate venture capital and why alignment of goals is paramount[27:18] The importance of staying true to your business vision and not blindly following VC adviceThe non-profit organization that Steve is passionate about: Woodside WildebeestsAbout Steve PretreSteve Pretre is a partner at World Innovation Lab. He is a veteran of multiple successful startups and has deep operating experience across product development, marketing, and strategic planning. Prior to joining World Innovation Lab, Steve was the co-founder and CEO of Metromile, an early innovator that paved the path for the current wave of insurance startups. He also held executive roles at Asurion, leading their mobile applications business unit as the company grew.About World Innovation LabWorld Innovation Lab is a venture capital fund supported by various governments and global corporations. WiL invests in companies looking to expand into new markets. They assist US startups in entering Japan and Asia and support Japanese startups in global expansion. Notable recent direct investments include Algolia, Asana, Automation Anywhere, Auth0, DataRobot, Kong, Mercari, MURAL, TransferWise, and Unqork. WiL also supports established and emerging venture funds. Additionally, they collaborate with corporate investors to enhance innovation through new business creation, startup partnerships, and cultural change. WiL acts as a bridge between startups and corporations in key innovation hubs globally, initially focusing on Japan and the US. Subscribe to our podcast and stay tuned for our next episode.

Irish-based data science and AI startup Zerve has raised $3.8 million in its pre-seed funding to build a new architecture for Data Science & AI development. The company has created a coding platform tailored for data science and AI development that allows teams to collaborate and share their outputs more easily. Zerve's cloud-based, serverless technology utilizes a novel, stateful architecture to create a scalable, collaborative development environment. The tool helps break down the silos that can exist between data scientists and developers. Zerve was co-founded in 2021, by college friends Phily Hayes (ex-LearnUpon and Deloitte) and Jason Hillary, PhD, Engineering, and later joined by Greg Michaelson, former Chief Customer Officer of DataRobot. The company is currently one of ten startups chosen from Europe to participate in Intel's Ignite Accelerator program for deep-tech companies. Since 2019, the 148 companies that have gone through the programme have raised over 1.6bn. Phily Hayes, CEO & co-founder of Zerve said, "Data scientists have never really been able to seamlessly share both their code and their results with their colleagues. The existing tools available are fragmented. It makes it hard to be productive. With Zerve, all the teams can collaborate live and build something stable enough to deploy. In much the same way as Figma made design collaborative, Zerve is poised to bring this innovation in the data science coding space." Greg Michaelson, co-founder of Zerve said, "Zerve brings about an architectural paradigm shift. We're dedicated to building Zerve to not only enhance the capabilities of data science but also to empower data scientists to deliver effortlessly in the era of AI." Elkstone Ventures (Flipdish, LetsGetChecked, Manna) led the funding round, with contributions from prominent angel investors, such as Sean Mullaney, CTO of Algolia, and Rob Hickey, former EVP of Engineering at DataRobot. Niall McEvoy, Partner at Elkstone Ventures said, "Zerve has an ambitious vision of bridging the gap between data science and AI development. The team has built a technology that will really allow companies to break down silos and harness the power of AI and data science and has the potential to do to data science what Figma did for design". Zerve currently has 12 employees and aims to hire across Engineering, Cloud Infrastructure, and Research and Development. See more stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

Today's guest is Ted Kwartler, Field CTO of Generative AI for DataRobot. DataRobot is an AI-powered software company that helps enterprises automate processes from end to end. In conversation with Emerj CEO and Head of Research Daniel Faggella, Ted gives a broader view of the data governance process for financial services. Throughout the episode, Ted offers financial leaders alternatives to popular approaches in Centers of Excellence in the form of data science professional resources groups that can better facilitate collaboration across the enterprise. Later, he explains where financial services organizations often make mistakes in scaling data management operations and how they can course correct before it's too late. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

In this episode, I learn so much from Nisha Talagala.Nisha Talagala is the CEO and founder of AIClub.World (https://corp.aiclub.world) which brings AI Literacy to  K-12 students, professionals, and other individuals worldwide. Nisha has significant experience in introducing technologies like Artificial Intelligence to new learners from students to professionals.  Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing Machine Learning in production for enterprises - acquired by DataRobot. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, NVM Software Lead, and Intel and CTO of Gear6. Nisha has more than 20 years of expertise in enterprise software development, distributed systems, technical strategy, and product leadership. Nisha earned her Ph.D. at UC Berkeley in Computer Science, holds 75 patents, over 25 refereed research publications, is a frequent speaker at industry and academic events, and is a contributing writer to Forbes and other publications. AIClub.World: https://corp.aiclub.worldLinkedIn: https://www.linkedin.com/in/nisha-talagala-6a6b20/LISTEN NOW:Apple Podcast – Find this episode and all the previous episodes on Apple PodcastSpotify – Find this episode and previous episodes of the show on Spotify!YouTube - Challenges & GoalsThe challenge of making everyone AI literate due to its increasing prevalence in everyday life. The goal of making AI more accessible to students and educators alike, fostering interest in AI among young people, and encouraging diverse voices in the field.Surprising Takeaways A surprising takeaway from the discussions is that within a short period, students can build their first AI model without any coding involved. Another unexpected insight is that despite initial fears or apprehensions about teaching AI, it's possible for educators to start small and gradually build up their knowledge base.Emerging PatternsImportance of demystifying AI for both teachers and studentsEmphasis on hands-on learning and problem-solvingUse of real-world applications of AI to engage student interestEncouragement of diversity within the fieldRecognition of everyone's role in shaping how technology evolvesKey Moments"We believed that we will reach a point which we are currently at where, you know, everybody…is going to need to become A I literate." "All students build their first A I in their first class… they've talked to it…" "I think it is scary and it is a bit overwhelming… I would encourage people to jump in with something small." "[AI] is important for people personally. It's important as teachers… And also important for humanity that every human says something because someone's gonna make these decisions."

Chris Riley serves as President of Worldwide Field Operations of DataRobot, where he is responsible for accelerating the company's revenue growth and global footprint, bringing DataRobot to organizations across all industries and geographies. Riley previously served as Chief Revenue Officer for Robotic Process Automation (RPA) leader Automation Anywhere, where he led global sales in over 90 countries and managed an ecosystem of over 1,900 partners. Prior to Automation Anywhere, Riley served as President of Global Sales at Dell Technologies, where he was responsible for a multi-billion dollar route to market. Riley also served as Vice President and General Manager for HP's storage business at HPE. Riley lives in Naples, FL with his family.In this conversation with John McMahon, Chris discusses the importance of understanding the culture and processes of a new organization when stepping into a leadership role. He emphasizes the need for leaders to be adaptable and willing to learn from their teams. Chris also highlights the significance of metrics and productivity in managing a sales organization, as well as the importance of hiring the right people and providing them with the necessary training and development. He shares insights on building trust with the sales team, prioritizing pipeline generation, and the role of mentors, coaches, and godfathers in a leader's career.HERE ARE SOME KEY SECTIONS TO CHECK OUT:[00:01:51] Overview of what DataRobot does and its applications[00:15:13] The importance of work ethic, coachability, and aptitude in hiring[00:23:47] Characteristics of successful leaders and the consequences of failure[00:38:48] The most critical step in the sales process and the importance of a champion[00:47:26] Navigating a sluggish economy and advice for salespeople[00:54:15] The importance of hard work, generating pipeline, and following the processHIGHLIGHT QUOTES[00:22:02] "A mentor is somebody you can speak openly without fear of judgment." - Chris Riley[00:36:35] “A good leader builds trust with the team and shows genuine interest in their success.” - Chris RileyLearn more about Chris through this link:https://www.linkedin.com/in/chrisriley3/Check out John McMahon's book here: https://www.amazon.com/Qualified-Sales-Leader-Proven-Lessons/dp/0578895064

Summary Real-time data processing has steadily been gaining adoption due to advances in the accessibility of the technologies involved. Despite that, it is still a complex set of capabilities. To bring streaming data in reach of application engineers Matteo Pelati helped to create Dozer. In this episode he explains how investing in high performance and operationally simplified streaming with a familiar API can yield significant benefits for software and data teams together. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Modern data teams are using Hex to 10x their data impact. Hex combines a notebook style UI with an interactive report builder. This allows data teams to both dive deep to find insights and then share their work in an easy-to-read format to the whole org. In Hex you can use SQL, Python, R, and no-code visualization together to explore, transform, and model data. Hex also has AI built directly into the workflow to help you generate, edit, explain and document your code. The best data teams in the world such as the ones at Notion, AngelList, and Anthropic use Hex for ad hoc investigations, creating machine learning models, and building operational dashboards for the rest of their company. Hex makes it easy for data analysts and data scientists to collaborate together and produce work that has an impact. Make your data team unstoppable with Hex. Sign up today at dataengineeringpodcast.com/hex (https://www.dataengineeringpodcast.com/hex) to get a 30-day free trial for your team! Your host is Tobias Macey and today I'm interviewing Matteo Pelati about Dozer, an open source engine that includes data ingestion, transformation, and API generation for real-time sources Interview Introduction How did you get involved in the area of data management? Can you describe what Dozer is and the story behind it? What was your decision process for building Dozer as open source? As you note in the documentation, Dozer has overlap with a number of technologies that are aimed at different use cases. What was missing from each of them and the center of their Venn diagram that prompted you to build Dozer? In addition to working in an interesting technological cross-section, you are also targeting a disparate group of personas. Who are you building Dozer for and what were the motivations for that vision? What are the different use cases that you are focused on supporting? What are the features of Dozer that enable engineers to address those uses, and what makes it preferable to existing alternative approaches? Can you describe how Dozer is implemented? How have the design and goals of the platform changed since you first started working on it? What are the architectural "-ilities" that you are trying to optimize for? What is involved in getting Dozer deployed and integrated into an existing application/data infrastructure? How can teams who are using Dozer extend/integrate with Dozer? What does the development/deployment workflow look like for teams who are building on top of Dozer? What is your governance model for Dozer and balancing the open source project against your business goals? What are the most interesting, innovative, or unexpected ways that you have seen Dozer used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dozer? When is Dozer the wrong choice? What do you have planned for the future of Dozer? Contact Info LinkedIn (https://www.linkedin.com/in/matteopelati/?originalSubdomain=sg) @pelatimtt (https://twitter.com/pelatimtt) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Dozer (https://getdozer.io/) Data Robot (https://www.datarobot.com/) Netflix Bulldozer (https://netflixtechblog.com/bulldozer-batch-data-moving-from-data-warehouse-to-online-key-value-stores-41bac13863f8) CubeJS (http://cube.dev/) Podcast Episode (https://www.dataengineeringpodcast.com/cubejs-open-source-headless-data-analytics-episode-248/) JVM == Java Virtual Machine (https://en.wikipedia.org/wiki/Java_virtual_machine) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57/) Airbyte (https://airbyte.com/) Podcast Episode (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) LMDB (http://www.lmdb.tech/doc/) Vector Database (https://thenewstack.io/what-is-a-real-vector-database/) LLM == Large Language Model (https://en.wikipedia.org/wiki/Large_language_model) Rockset (https://rockset.com/) Podcast Episode (https://www.dataengineeringpodcast.com/rockset-serverless-analytics-episode-101/) Tinybird (https://www.tinybird.co/) Podcast Episode (https://www.dataengineeringpodcast.com/tinybird-analytical-api-platform-episode-185) Rust Language (https://www.rust-lang.org/) Materialize (https://materialize.com/) Podcast Episode (https://www.dataengineeringpodcast.com/materialize-streaming-analytics-episode-112/) RisingWave (https://www.risingwave.com/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) DataFusion (https://docs.rs/datafusion/latest/datafusion/) Polars (https://www.pola.rs/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Feature engineering is a crucial aspect of the machine learning workflow. To make that possible, there are a number of technical and procedural capabilities that must be in place first. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Razi Raziuddin about how data engineers can empower data scientists to develop and deploy better ML models through feature engineering Interview Introduction How did you get involved in the area of data management? What is feature engineering is and why/to whom it matters? A topic that commonly comes up in relation to feature engineering is the importance of a feature store. What are the tradeoffs for that to be a separate infrastructure/architecture component? What is the overall lifecycle of a feature, from definition to deployment and maintenance? How is this distinct from other forms of data pipeline development and delivery? Who are the participants in that workflow? What are the sharp edges/roadblocks that typically manifest in that lifecycle? What are the interfaces that are needed for data scientists/ML engineers to be able to self-serve their feature management? What is the role of the data engineer in supporting those interfaces? What are the communication/collaboration channels that are necessary to make the overall process a success? From an implementation/architecture perspective, what are the patterns that you have seen teams build around for feature development/serving? What are the most interesting, innovative, or unexpected ways that you have seen feature platforms used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on feature engineering? What are the resources that you find most helpful in understanding and designing feature platforms? Contact Info LinkedIn (https://www.linkedin.com/in/razi-raziuddin-7836301/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links FeatureByte (https://featurebyte.com/) DataRobot (https://www.datarobot.com/) Feature Store (https://www.featurestore.org/) Feast Feature Store (https://feast.dev/) Feathr (https://github.com/feathr-ai/feathr) Kaggle (https://www.kaggle.com/) Yann LeCun (https://en.wikipedia.org/wiki/Yann_LeCun) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

In this episode, I had the pleasure of speaking with Hamel Husain. Hamel is a machine learning and MLOps extraordinaire, he was one of the core maintainers of Fast.ai and has worked on ML and MLOps in places like Data Robot, Airbnb, and GitHub. We talk about Large Language Models, the future role of data scientists in the world of LLMs, and Hamel's approach to solving MLOps problems. Watch the video: https://www.youtube.com/watch?v=3oElMXPkaVs Relevant Links:

Is Bubba Wallace the new Danica Patrick?  Way to ruin what little credibility you had left, schmuck.  We prefer Jim Brown - R.I.P., big guy.  We also prefer Richard Jewell (speaking of R.I.P.), whom this episode somehow commingles with the Unabomber (who will probably not be resting peacefully, if you catch our drift).  Then it's all experts and flying SFPD patrol cars and NYC suing carmakers who don't make cars that can survive the city's criminals, plus not blowing people up. Don't worry, there's more, like a complete “16 Volt” background track, journalists behaving badly, “Escape from New York”, “Invasion USA”, Kurt Russell, Chuck Norris, Lee Van Cleef, Mac 10s, and more corruption in Quid Pro Joe's basement.

Is Bubba Wallace the new Danica Patrick?  Way to ruin what little credibility you had left, schmuck.  We prefer Jim Brown - R.I.P., big guy.  We also prefer Richard Jewell (speaking of R.I.P.), whom this episode somehow commingles with the Unabomber (who will probably not be resting peacefully, if you catch our drift).  Then it's all experts and flying SFPD patrol cars and NYC suing carmakers who don't make cars that can survive the city's criminals, plus not blowing people up. Don't worry, there's more, like a complete “16 Volt” background track, journalists behaving badly, “Escape from New York”, “Invasion USA”, Kurt Russell, Chuck Norris, Lee Van Cleef, Mac 10s, and more corruption in Quid Pro Joe's basement.

Data scientist, international policy advisor on economic growth, and social entrepreneur Belén Sánchez Hidalgo talks about her passion to bring diversity to AI. While working as a consultant at The World Bank, Belén became anxious about the potential risks of Artificial Intelligence and decided to do something about it. She left her career in public policy to become a data scientist at DataRobot. Here, she became inspired to educate women on AI development and created WaiCAMP, an initiative that closes the AI gender gap in Latin America through pragmatic education.  Originally from Ecuador, where she graduated from Pontificia Universidad Católica del Ecuador with various financial degrees, Belén went on to study Enterprise Development at Leipzig University and obtain a Master in Public Administration from The Harvard Kennedy School.

We are having another LLMs in-production Virtual Conference. 50+ speakers combined with in-person activities around the world on June 15 & 16. Sign up free here: https://home.mlops.community/home/events/llm-in-prod-part-ii-2023-06-20 // Abstract This panel discussion is centered around a crucial topic in the tech industry - data privacy and security in the context of large language models and AI systems. The discussion highlights several key themes, such as the significance of trust in AI systems, the potential risks of hallucinations, and the differences between low and high-affordability use cases. The discussion promises to be thought-provoking and informative, shedding light on the latest developments and concerns in the field. We can expect to gain valuable insights into an issue that is becoming increasingly relevant in our digital world. // Bio Diego Oppenheimer Diego Oppenheimer is an entrepreneur, product developer, and investor with an extensive background in all things data. Currently, he is a Partner at Factory a venture fund specializing in AI investments as well as interim head of product at two LLM startups. Previously he was an executive vice president at DataRobot, Founder, and CEO at Algorithmia (acquired by DataRobot), and shipped some of Microsoft's most used data analysis products including Excel, PowerBI, and SQL Server. Diego is active in AI/ML communities as a founding member and strategic advisor for the AI Infrastructure Alliance and MLops.Community and works with leaders to define ML industry standards and best practices. Diego holds a Bachelor's degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University Gevorg Karapetyan Gevorg Karapetyan is the co-founder and CTO of ZERO Systems where he oversees the company's product and technology strategy. He holds a Ph.D. in Computer Science and is the author of multiple publications, including a US Patent. Vin Vashishta C-level Technical Strategy Advisor and Founder of V Squared, one of the first data science consulting firms. Our mission is to provide support and clarity for our clients' complete data and AI monetization journeys. Over a decade in data science and a quarter century in technology building and leading teams and delivering products with $100M+ in ARR. Saahil Jain Saahil Jain is an engineering manager at You.com. At You.com, Saahil builds search, ranking, and conversational AI systems. Previously, Saahil was a graduate researcher in the Stanford Machine Learning Group under Professor Andrew Ng, where he researched topics related to deep learning and natural language processing (NLP) in resource-constrained domains like healthcare. Prior to Stanford, Saahil worked as a product manager at Microsoft on Office 365. He received his B.S. and M.S. in Computer Science at Columbia University and Stanford University respectively. Shreya Rajpal Shreya is the creator of Guardrails AI, an open-source solution designed to establish guardrails for large language models. As a founding engineer at Predibase, she helped build the Applied ML and ML infra teams. Previously, she worked at Apple's Special Projects Group on cross-functional ML, and at Drive.ai building computer vision models.

In this episode of In AI we Trust? Dr. Haniyeh Mahmoudian, Global AI Ethicist at DataRobot, provides insight into the timely and critical role of an AI ethicist. Haniyeh explains how culture is a key element of responsible AI development. She also reflects on the questions to ask in advance of designing an AI model and the importance of engaging multiple stakeholders to design AI effectively. Tune in to this episode to learn these and other insights from an industry thought leader.—Resources mentioned in this episode: How to Tackle AI Bias (Haniyeh Mahmoudian, PhD)

In our conversation, we learn about her professional journey and how this led to her working at DataRobot, what she realized was missing from the DataRobot platform, and what she did to fill the gap. We discuss the importance of bias in AI models, approaches to mitigate models against bias, and why incorporating ethics into AI development is essential. We also delve into the different perspectives of ethical AI, the elements of trust, what ethical “guard rails” are, and the governance side of AI. Key Points From This Episode:Dr. Mahmoudian shares her professional background and her interest in AI.How Dr. Mahmoudian became interested in AI ethics and building trustworthy AI.What she hopes to achieve with her work and research. Hear practical examples of how to build ethical and trustworthy AI.We unpack the ethical and trustworthy aspects of AI development.What the elements of trust are and how to implement them into a system.An overview of the different essential processes that must be included in a model.How to mitigate systems from bias and the role of monitoring.Why continual improvement is key to ethical AI development.Find out more about DataRobot and Dr. Mahmoudian's multiple roles at the company.She explains her approach to working with customers.Discover simple steps to begin practicing responsible AI development.Tweetables:“When we talk about ‘guard rails' sometimes you can think of the best practice type of ‘guard rails' in data science but we should also expand it to the governance and ethics side of it.” — @HaniyehMah [0:11:03]“Ethics should be included as part of [trust] to truly be able to think about trusting a system.” — @HaniyehMah [0:13:15]“[I think of] ethics as a sub-category but in a broader term of trust within a system.” — @HaniyehMah [0:14:32]“So depending on the [user] persona, we would need to think about what kind of [system] features we would have .” — @HaniyehMah [0:17:25]Links Mentioned in Today's Episode:Haniyeh Mahmoudian on LinkedInHaniyeh Mahmoudian on TwitterDataRobotNational AI Advisory CommitteeHow AI HappensSama

Devin Williams, VP of Sales at DataRobot, joins KD in this edition of the Live Better Sell Better podcast. Emotional intelligence is thrown around as something that is related to soft skills. But is it something that can actually be taught, much like a hard skill?Devin defines the Emotional Quotient (EQ) or emotional intelligence and what it means for sales professionals. He talks about the connection between self-awareness and social awareness and how both essentially make our complexities as human beings.HIGHLIGHT QUOTESSelf-awareness can be scary because it involves self-inspection - Devin: "You can't get started on self-awareness until you're ready to take a hard look in the mirror and agree to go make some changes."What else can you do to improve social awareness? - Devin: "The more questions that you can ask in a genuine and authentic level of curiosity, the more your social awareness is going to improve. Because you've put in the effort and work to grow your self-awareness, you are now more able to see the world for what it really is."You can find out more about Devin in the links below:LinkedIn: https://www.linkedin.com/in/devinwilliamspfp/Non-Profit: https://www.peoplefirstprofessionals.org/Live Better. Sell Better. is sponsored by our proud partner:Rocket Reach | rocketreach.com

Vulnerability is an important quality often overlooked in the world of tech. Yet being vulnerable and authentic helps you to set realistic expectations, speak with executives on a human level, and connect with your audience. In this episode, Satyen interviews Jepson (Ben) Taylor, Chief AI Strategist at Dataiku. Jepson is a visionary in the advancements of AI, ML, and data science. Prior to joining Dataiku, he served as the Chief AI Evangelist at DataRobot and co-founded the deep learning startup Zeff.ai, which was acquired by DataRobot in 2020. Jepson is a frequent industry speaker and collaborates with the data science community to improve AI and deep learning. Satyen and Jepson discuss the power of failure, the lie of job security, and proving data's worth through storytelling.--------"If nothing is failing, then it's not a very innovative company, not a very innovative culture. So there is a fraction of failure that for a mature organization you should celebrate. But with failure, you have the time urgency. How can we fail faster? I'd rather fail this week than four months from now." – Jepson Taylor--------Time Stamps:*(03:25): The special ingredient for speaking with executives*(11:52): Learning to embrace the expectation of failure and failing fast*(18:13): The two rules of good storytelling: authenticity and knowing your audience*(30:47): Jepson's advice for joining a startup and the lie of job security*(44:08): The importance of celebrating the soul of your user*(50:43): Satyen's Takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://www.alation.com/podcast/* Alation's LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen's LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksFollow Jepson on LinkedInFollow Jepson on Twitter

David “Gonzo” González is an Executive and Data Science professional with well over a decade of experience putting AI and ML into production in mission-critical systems. He co-founded Zeff.ai, acquired in 2020 by DataRobot where he succeeded in bringing a jobs-to-be-done perspective to focus product strategy. He led Zeff.ai as CEO and pioneered transactionally authored training and inference on multi-modal datasets. Gonzo is the primary author of The AI Manifesto for Applied Artificial Intelligence Development and has multiple ML patents. In his free time, he enjoys playing outdoors in the wilds of his adoptive home of Utah with his wife and five children. Gonzo has recently taken a break to pursue a startup combining health and wellness and AI. As a warning, in this episode we touch on sensitive topics like a data centric view of religion and also on plant based medicine. This was an enlightening conversation and I'm grateful for Gonzos candidness and openness. Now on to the interview. 

About Sean Plankey: Sean Plankey currently serves as the Chief Architect for BedRock Systems, leading efforts to utilize BedRock's formal methods proven software isolation secure platform to solve the most pressing cybersecurity problems across industry and government. Prior to BedRock Systems Sean served as the Public Sector CTO at DataRobot, a Silicon Valley Artificial Intelligence Platform. In government, Sean served as the Principal Deputy Assistant Secretary for Cybersecurity, Energy Security, and Emergency Response at the Department of Energy. In this role he led the design and implementation of DOE's cybersecurity supply chain program CyTRICS. Mr. Plankey also served on the National Security Council as the Director for Maritime and Pacific Cybersecurity Policy, where he co-authored the National Maritime Cybersecurity Plan and multiple Presidential Directives on offensive cyberspace operations. He has also served as the Global Cyber Intelligence Advisor for BP plc, and as the Deputy Chief Information Officer for U.S. Navy Intelligence. He is a 2003 graduate of the United States Coast Guard Academy and a 2008 graduate of the University of Pennsylvania.In this episode, Aaron and Sean Plankey discuss:How do we protect critical infrastructure? The potential risk of public EV charging stations What kind of technology might people invest in? Understanding our supply chains and economic dependenciesKey Takeaways:The majority of the critical infrastructure in the U.S. is owned by the private selector. Therefore, cyber security in critical infrastructures is semi-regulated. Meaning, any changes made will need the collaboration of both government and the private sector.The installation of public EV charging stations along roads requires careful consideration of cybersecurity concerns. The connection between the charging stations, electric vehicles, computers, networks, and physical grid creates potential risks, such as fire hazard, reliability issues with the grid and other issues. When facing a limited budget, the decision between investing in efficiency optimization versus cybersecurity often results in a focus on efficiency. Unfortunately, cybersecurity is often perceived as a cost and its benefits may not be as tangible or easily understood.Currently, the U.S. is granting adversary space in our digital terrain. It poses a lot of risk for us to be the customer of an adversary. We have to know our customers and look more into our economic dependencies. "Now you have access and you have a myriad of connectivity. And you're doing that analysis in the fog. Asset management is very difficult and that's where we need to bring that security. We'll continue to see that increasing IT/OT convergence in the fog. And that's where we protect. " — Sean PlankeyConnect with Sean Plankey:LinkedIn: https://www.linkedin.com/in/sean-plankey/ Connect with Aaron:LinkedIn: https://www.linkedin.com/in/aaronccrowLearn more about Industrial Defender:Website: https://www.industrialdefender.com/podcast LinkedIn: https://www.linkedin.com/company/industrial-defender-inc/Twitter: https://twitter.com/iDefend_ICSYouTube: https://www.youtube.com/@industrialdefender7120Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it.

In this episode of the Thoughtful Entrepreneur, your host Josh Elledge speaks to the Founder and CEO ofhttps://greenerprocess.com/ ( )https://www.trustscience.com/ (Trust Science), https://www.linkedin.com/in/evanchrapko/ (Evan Chrapko). Trust Science aid preexisting data scientists or risk departments in assessing borrowers' profiles. They can be the entire solution for smaller businesses or those who have never had experience working with a bureau before because of low score levels. They bring automation and automated decision-making in a high degree of predictability on creditworthiness.  Evan gave out some indicators of what to look at when assessing an individual's creditworthiness. He also talked through the challenges in the conventional system and how Trust Science comes into play to close the gap. Working with the Trust Science system helps their customers find a lender customer worldwide. Key points from the episode: What industry does Trust Science work with? Why are old formula credits a bit problematic? Indicators to look at to assess creditworthiness. How did Trust Science come about? Business growth opportunities for Trust Science. About Evan Chrapko: Evan is a serial entrepreneur and investor having served as CEO or advisor for numerous innovative start-ups including FloNetworks (acquired by DoubleClick for $80 Million) and PlateSpin (acquired by Novell for $205 Million). Prior to Trust Science, Evan and his brother Shane founded and, within 30 months, sold cloud storage pioneer DocSpace for $568 million. Evan is a CPA, CA and holds a Juris Doctor (Law Degree) from Columbia University. He is a Henry Crown Fellow of the Aspen Institute, a member of YPO, and he has been named to the Real Leaders Global 100 (alongside Bill Gates, Richard Branson, Elon Musk, Peter Diamandi,s et. al.) and a Top 50 AI CEO (alongside others like the CEO of Data Robot.)   About Trust Science: Organizations in many industries rely on credit scores to gauge amounts and types of credit that they will offer to an individual. Those scores purport to provide meaningful information about how much debt a person should be able to take on and what the level of risk (the borrower's probability of default) will be. However, the information from old credit bureaus isn't always deep enough for a meaningful assessment, in part because it relies on outdated types of data & calculation methods.  This is especially punishing for the financially disadvantaged or N2C (New to Country/New to Credit) person. Credit Bureau 2.0, a FinTech SaaS platform, is the solution to this problem, harnessing alternative data and explainable AI for  more accurate and precise credit assessments. This is enabling lenders to find more trustworthy borrowers and letting borrowers gain access to credit based on information that traditional credit bureaus aren't taking into account. Links Mentioned in this Episode: Want to learn more? Check out the Trust Science website at https://www.trustscience.com/ (https://www.trustscience.com/) Check out Greener Trust Science on LinkedIn at https://www.linkedin.com/company/trust-science/ (https://www.linkedin.com/company/trust-science/) Check out Evan Chrapko on LinkedIn at  https://www.linkedin.com/in/evanchrapko/ (https://www.linkedin.com/in/evanchrapko/) Don't forget to subscribe to The Thoughtful Entrepreneur and thank you for listening. Tune in next time! More from UpMyInfluence: Tickets for the FIRST Annual Fame and Profit Summit 2022 are on sale now! https://fameandprofit.com/tickets (Check it out HERE!) ️  We are actively booking guests for our The Thoughtful Entrepreneur.https://upmyinfluence.com/guest ( Schedule HERE). Are you a 6-figure consultant? I've got high-level intros for you.https://upmyinfluence.com/b2b ( Learn more here). What is your #1 Lead Generation BLOCKER? http://upmyinfluence.com/quiz (Take my free quiz here).

Read the full transcriptHow can we change the way we think about expertise (or the trustworthiness of any information source) using forecasting? How do prediction markets work? How can we use prediction markets in our everyday lives? Are prediction markets more trustworthy than large or respectable news outlets? How long does it take to sharpen one's prediction skills? In (e.g.) presidential elections, we know that the winner will be one person from a very small list of people; but how can we reasonably make predictions in cases where the outcomes aren't obviously multiple-choice (e.g., predicting when artificial general intelligence will be created)? How can we move from the world we have now to a world in which people think more quantitatively and make much better predictions? What scoring rules should we use to keep track of our predictions and update accordingly?Peter Wildeford is the co-CEO of Rethink Priorities, where he aims to scalably employ a large number of well-qualified researchers to work on the world's most important problems. Prior to running Rethink Priorities, he was a data scientist in industry for five years at DataRobot, Avant, Clearcover, and other companies. He is also recognized as a Top 50 Forecaster on Metaculus (international forecasting competition) and has a Triple Master Rank on Kaggle (international data science competition) with top 1% performance in five different competitions. Follow him on Twitter at @peterwildeford.Further reading:ClearerThinking.org's "Calibrate Your Judgment" practice programMetaculus (forecasting platform)Manifold MarketsPolymarket"Calibration Scoring Rules for Practical Prediction Training", a paper by Spencer Greenberg

To improve Data Literacy, organizations need high-quality data training programs that give their employees the most valuable and relevant data skills they need. Many companies fall into the trap of implementing training programs that are poorly designed or not relevant for the needs of their learners. Sharon Castillo is the VP of Global Education at DataRobot, where she developed the DataRobot University, a self-service education portal that features both free and paid courses on AI and machine learning that are available to the public. With over 30 years of experience, Sharon is a leading expert in data training and employee upskilling programs, from development through execution. Sharon joins the show to talk about what makes an effective data training program, how to ensure employees retain the information, how to properly incentivize training participation, why organizations should prioritize training, and much more. This is essential listening for anyone developing a training program for their team or organization.