Podcast appearances and mentions of sam charrington

MLOps Coffee Sessions #172 with LLMs in Production Conference part 2 Building LLM Products Panel, George Mathew, Asmitha Rathis, Natalia Burina, and Sahar Mor Using hosted by TWIML's Sam Charrington. We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O // Abstract There are key areas we must be aware of when working with LLMs. High costs and low latency requirements are just the tip of the iceberg. In this panel, we hear about common pitfalls and challenges we must keep in mind when building on top of LLMs. // Bio Sam Charrington Sam is a noted ML/AI industry analyst, advisor and commentator, and host of the popular TWIML AI Podcast (formerly This Week in Machine Learning and AI). The show is one of the most popular Tech podcasts with nearly 15 million downloads. Sam has interviewed over 600 of the industry's leading machine learning and AI experts and has conducted extensive research into enterprise AI adoption, MLOps, and other ML/AI-enabling technologies. George Mathew George is a Managing Director at Insight Partners focused on venture-stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit. Asmitha Rathis Asmitha is a Machine Learning Engineer with experience in developing and deploying ML models in production. She is currently working at an early-stage startup, PromptOps, where she is building conversational AI systems to assist developers. Prior to her current role, she was an ML engineer at VMware. Asmitha holds a Master's degree in Computer Science from the University of California, San Diego, with a specialization in Machine Learning and Artificial Intelligence. Natalia Burina Natalia is an AI Product Leader who was most recently at Meta, leading Responsible AI. During her time at Meta, she led teams working on algorithmic transparency and AI Privacy. In 2017 Natalia was recognized by Business Insider as “The Most Powerful Female Engineer in 2017”. Natalia was also an Entrepreneur in Residence at Foundation Capital, advising portfolio companies and working with partners on deal flow. Prior to this, she was the Director of Product for Machine Learning at Salesforce, where she led teams building a set of AI capabilities and platform services. Prior to Facebook and Salesforce, Natalia led product development at Samsung, eBay, and Microsoft. She was also the Founder and CEO of Parable, a creative photo network bought by Samsung in 2015. Natalia started her career as a software engineer after pursuing Bachelor's degree in Applied and Computational Mathematics from the University of Washington. Sahar Mor Sahar is a Product Lead at Stripe with 15y of experience in product and engineering roles. At Stripe, he leads the adoption of LLMs and the Enhanced Issuer Network - a set of data partnerships with top banks to reduce payment fraud. Prior to Stripe he founded a document intelligence API company, was a founding PM in a couple of AI startups, including an accounting automation startup (Zeitgold, acq'd by Deel), and served in the elite intelligence unit 8200 in engineering roles. Sahar authors a weekly AI newsletter (AI Tidbits) and maintains a few open-source AI-related libraries (https://github.com/saharmor). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Learn about the applications of AI with Sam Charrington, Founder and Principal Analyst, TWIML AI Podcast (This Week in Machine Learning and AI).

Sam Charrington (@samcharrington, host @twimlai podcast) talks about the evolution of AI & ML in 2023, and the possibilities of ChatGPT.SHOW: 684CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OTHER NEW PODCAST - "CLOUDCAST BASICS"SHOW SPONSORS:Datadog Synthetic Monitoring: Frontend and Backend Modern MonitoringEnsure frontend issues don't impair user experience by detecting user-facing issues with API and browser tests with a free 14 day Datadog trial. Listeners of The Cloudcast will also receive a free Datadog T-shirt. Eaton HomepageEaton and Tripp Lite have joined forces to bring more sanity to IT pros days, every day. Visit www.eaton.com/audio to learn more!SHOW NOTES:This Week in AI & ML (podcast, homepage)Sam talks to ChatGPT to create a podcast(2017) - Sam on Eps.321(2019) - Sam on Eps.382(2020) - Sam on Eps.437Topic 1 - Hi Sam. Welcome back to the show. Before we get into today's discussions, tell us a little bit about the breadth of things you're doing over at TWIMLAI and how people can get involved?Topic 2 - Let's start with the recent headlines - Stable Diffusion and ChatGPT. For those of us that aren't around this everyday, it feels like some big leaps in useability. Did we see a big jump in AI at the end of 2022, or was this just good UI design, or something else?Topic 3 - ChatGPT - Give us the basics, and then what are some of the more interesting things you're hearing people start to talk about with technology like this.Topic 4 - The last time you were on, we talked about how “the goal posts tend to shift” about what we think of as powerful AI. Do you see any areas of AI getting close to becoming a big shift in terms of ease-of-use, or being hidden/embedded in other technologies?Topic 5 - There are various reports about GPUs being harder to get these days. There is also speculation that the cost of AI processing has been going up. What are you seeing in terms of cost of processing vs. useful outcomes lately? Topic 6 - What's the “getting started” curve look like for companies that want/need to add or integrate AI & ML into their applications? What's the barrier to entry and has it changed in the last few years? What are some numbers you hear about cost of engineers, sizes of datasets, number of experiments and models needed to run, etc.? Topic 7 - What are some of the things you're really looking forward to in 2023, whether it's technology or trends or something else?FEEDBACK?Email: show at the cloudcast dot netTwitter: @thecloudcastnet

Much of the way we talk and think about MLOps comes from the perspective of large consumer internet companies like Facebook or Google. If you work at a FAANG company, these approaches might work well for you. But what about if you work at one of the many small, B2B companies that stand to benefit through the use of machine learning? How should you be thinking about MLOps and the ML lifecycle in that case? In this live podcast interview from TWIMLcon: AI Platforms 2022, Sam Charrington explores these questions with Jacopo Tagliabue, whose perspectives and contributions on scaling down MLOps have served to make the field more accessible and relevant to a wider array of practitioners.

Sam Charrington, software engineer, entrepreneur and TWIML podcast host joins Cindy Moehring and sheds light on what lies behind the tech buzzwords. The conversation covers how machine learning works, the contextual and inherent risks that exist, the need for diversity in tech, reimagining the link between labor and livelihood, and start ups to watch.             Learn more about the Business Integrity Leadership Initiative by visiting our website at https://walton.uark.edu/business-integrity/      Links from the episode: https://twimlai.com/  (https://twimlai.com/ )      https://twimlai.com/ethics-bias-and-ai-twiml-episode-playlist/ (https://twimlai.com/ethics-bias-and-ai-twiml-episode-playlist/)     

Coffee Sessions #18 with Luigi Patruno of ML in Production, a Centralized Repository of Best Practices Summary Luigi Patruno and ML in production MLOps workflow: Knowledge sharing and best practices Objective: learn! Links: ML in production: https://mlinproduction.com/ Why you start MLinProduction: https://mlinproduction.com/why-i-started-mlinproduction/ Luigi Patruno: a man whose goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in production. Luigi shares with us why he started ML in Production - A lot irrelevant content; a lot of clickbait with low standards of quality. He had an Entrepreneurial itch and The solution was to start a weekly newsletter. From there he started creating Blog posts and now teamed up with Sam Charrington of TWIML to create courses on SagMaker ML. Applied ML Best practices Reading google and microsoft papers Analyzing the tools that are out there ie sagemaker and how to the see the world? Aimed at making you more effective and efficient at your job Community questions Taking some time to answer some community questions! Who do you learn from? Favorite resources? Self-taught, papers, talks Construct the systems Uber michelangelo -----------------

Today's episode is a recording of a LIVE interview discussion, hosted on LinkedIn LIVE, featuring a program called "The Example Show" produced by Ryan Swanstrom. Ryan is a heavily credentialed Software Engineer, with a Masters in Math from Iowa State University, and a PHD in Computational Science and Statistics. Ryan's experience spans across over 20 years of working with notable enterprises including Northrup Grumman, all the way through to organizations like Wells Fargo and Microsoft, and most recently, Ryan consults for various organizations on his own, while late and current projects focus on reporting and analytics related to buying pattern during the COVID Pandemic. Ryan is also the author of the oldest data science blog on the internet, Data Science 101, started in 2012."The Example Show", featured in today's episode, is a weekly live Interview show that dives into the challenges and the successes of creating a weekly/monthly show, LIVE streaming program, podcast, etc. Ryan brings on some really talented guests to talk about different elements of how and why they create content, and LIVE streams the interview on LinkedIn LIVE. Today Ryan brings Derek Russell, the host of the Data Binge Podcast, onto his show as a guest, to talk about the "Why" of creating a show.Theses discussed:-The "Why" of creating a show, why YOU should get started, and how the fastest way to learn is to being a new project.-The (3) things you must have aligned before you start a podcast or LIVE streaming program.-Real talk on monetization, how to prioritize your efforts based on outcomes, and the new skills, connections, and opportunities presented with podcast and LIVE stream content creation.Thank you for listening!The Why of Creating a Show Interview on YouTube: https://youtu.be/IgeNh6DP-Q4How to Contact Ryan Swanstrom:LinkedIn: https://www.linkedin.com/in/ryanswanstrom/The Example Show: https://www.ryanswanstrom.com/the-example-showRyan's Blog:DataScience101: https://101.datascience.community/Resources:Building a Podcast Community with Sam Charrington: https://www.ryanswanstrom.com/the-example-show-community-sam-charringtonTips Mentioned:Kindle Paperwhite: https://amzn.to/332Rqc9Free Library Book App: Libby: https://libbyapp.com/welcome--------------------------------Interested in starting your own podcast? Some candid advice here: https://www.linkedin.com/pulse/how-start-podcast-3-step-gono-go-beginners-guide-derek-russellLearn more about the Data Binge Podcast at www.thedatabinge.comConnect with Derek:LinkedIn: https://www.linkedin.com/in/derekwesleyrussell/Youtube: https://www.youtube.com/channel/UCN1c5mzapLZ55ciPgngqRMg/featuredInstagram: https://www.instagram.com/drussnetwork/Twitter: https://twitter.com/drussnetworkMedium: https://medium.com/@derekwesleyrussellEmail: derek@thedatabinge.com

Sam Charrington (@samcharrington, Host of TWIML & AI Podcast) talks about AI & ML trends in 2020, frameworks to understand usage patterns, hot new technology to explore, how long projects take to succeed, and the inherent bias built into every AI & ML model.SHOW: 437SHOW SPONSOR LINKS:Datadog Homepage - Modern Monitoring and AnalyticsTry Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirtMongoDB Homepage - The most popular database for modern applicationsMongoDB Atlas - MongoDB-as-a-Service on AWS, Azure and GCPCLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwSHOW NOTES:TWIML Homepage (Podcasts, eBooks, etc.)eBook: The Definitive Guide to ML PlatformsStudy Groups & Education TWIML Conference HomepageSam Charrington on The Cloudcast in 2019 (Eps.321) Topic 1 - Welcome back to the show. Let’s start with the broad set of TWIML activities that you’re working on these days. Topic 2 - You focus on AI & ML every week, across a lot of different domains and usages. It’s a broad scope. If you had to focus it on Enterprise/Business leaders, how do you structure a conversation around how to align business opportunity and technology choices? Topic 3 - What are some of the most commonly used technologies being deployed around AI/ML systems? Any big shifts over the last couple of years? Topic 4 - You’ve been around Cloud Computing and DevOps communities, which required companies to go through some people/process change to achieve success. What are the people/process changes that you typically see with AI/ML environments?Topic 5 - If somebody asked you how they can put a timeline on when they’ll see value around their AI/ML, is that a realistic ask? What are the factors that go into achieving success in AI/ML projects?Topic 6 - What are some of the interesting usages of AI/ML that you’ve seen in use recently?Topic 7 - There has been quite a bit of discussion recently about bias in AI/ML algorithms. Can you explain what this means and how it could impact the system’s decision making?FEEDBACK?Email: show at thecloudcast dot netTwitter: @thecloudcastnet

Who better to provide a CEO-level perspective on machine learning and artificial intelligence than Sam Charrington, the host of the wildly successful podcast This Week in Machine Learning & AI (TWIML & AI). In this episode, Jon Prial talks to Sam about the insights he's gleaned from interviewing more than 150 ML and AI experts about an array of fascinating topics. You'll learn about: - The macro trends in machine learning and AI - How mature enterprises are when it comes to ML and AI - Creating generalizable AI platforms - The last mile problem - The growing importance of trust and bias To learn more about this episode, check out the show notes: http://bit.ly/2yiI8IQ

Minter Dialogue Episode #340Sam Charrington is an industry analyst, specialized in Machine Learning and Artificial Intelligence. He's also the host of the very successful podcast, TWiMLAI aka This Week in Machine Learning and AI. In this conversation with Sam, we plunge into how and why businesses are using ML and AI, the biggest learnings Sam has had after doing nearly 300 episodes, who was his favorite guest, as well as the outlook for AI/ML in 2020. If you've got comments or questions you'd like to see answered, send your email or audio file to nminterdial@gmail.com; or you can find the show notes and comment on minterdial.com. If you liked the podcast, please take a moment to go over to iTunes or your favourite podcast channel, to rate/review the show. Otherwise, you can find me @mdial on Twitter. Support the show (https://www.patreon.com/minterdial)

On this episode of The New Stack Makers, TNS Founder & EiC Alex Williams sits down with Sam Charrington, Founder of This Week in Machine Learning & AI (TWiML & AI). The inaugural TWIMLcon conference takes place October 1st-2nd, 2019 at the Mission Bay Conference Center in San Francisco, California. TWIMLcon aims to bring a fresh perspective to AI & ML events, growing out of conversations that Charrington had with enterprises that, “Tended to be at a very interesting transition point.” Noting that he heard towards the end of last year that, “Companies kicked off a lot of machine learning proof-of-concept types of projects, they had some initial successes, their data science teams were out evangelising, and some of those proof-of-concepts were starting to mature [...] And so all of a sudden these organizations, they were challenged with the transition from ‘How do I successfully execute an individual machine learning project,' to ‘How do I become an engine for delivering machine learning at my organization?'” said Charrington.

If I had to pick one overarching theme from the 15 or so CRM industry events I’ve attended so far this year it would probably center around the crossroads of where automation and AI intersect. And during last month’s PegaWorld user conference I had a chance to speak with the founder and host of the very popular This Week in Machine Learning and Artificial Intelligence podcast Sam Charrington, who in just over three years has over five million show downloads. Sam shares his thoughts on we are today with technologies like machine learning, AI and natural language processing, and how companies are just scratching the surface in putting these tools to work to improve all aspects of their businesses, but have to be implemented with the proper perspective. And he also addresses another theme that I heard addressed at multiple conference this year; how AI will eventually lead to AE, artificial empathy.

On this short episode we follow up on a sage piece of advice from Sam Charrington. Namely, there is no one right way to learn all of the requisite skills to be successful in data science or the data profession. Whether you love data engineering, visualization, applied statistics, story telling, or data science, there are so many roads available to you to learn the necessary abilities. You can read books, take online courses, enroll in University programs (or some combination of the three). All that matters is that you have the excitement, drive, and passion to complete your training and enter the profession!To keep up with the podcast be sure to follow us on twitter @datacouturepod and on instagram @datacouturepodcast. And, if you'd like to help support future episodes, then consider becoming a patron at patreon.com/datacouture!Music for the show: Foolish Game / God Don't Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: SnowflakeSupport the show (https://www.patreon.com/datacouture)

On this episode we interview the inimitable Sam Charrington of "This Week in Machine Learning and Artificial Intelligence" fame. Sam's passion is helping people deliver, understand and adopt intelligent applications, systems, and technologies, including machine learning & AI, big data and cloud. Much of his work involves the application of a fortunate super-power: making complex concepts simple, clear, and compelling.He uses this in his podcast and speaking engagements to help data scientists, developers and enterprise innovators understand what’s here and coming next.He uses this in his research and end-user consulting to help business leaders understand the implications and opportunities presented by machine intelligence.He uses this to help podcast sponsors and vendor clients craft content, campaigns and strategies that grow their businesses and better connect, communicate and establish credibility with enterprise innovators and decision makers.Sam is a frequent commentator on technology and business issues: my columns have appeared in VentureBeat, The New Stack, ReadWriteWeb, and others, and he's frequently quoted in a variety of publications. Sam has presented at numerous conferences including Data Natives, AI Summit, Future of Data Summit, Interop ITX, Connect Expo Australia, DockerCon and many more.We are very happy to have had the opportunity to interview Sam, and we hope you had some fun!To keep up with the podcast be sure to follow us on twitter @datacouturepod and on instagram @datacouturepodcast. And, if you'd like to help support future episodes, then consider becoming a patron at patreon.com/datacouture!Music for the show: Foolish Game / God Don't Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: SnowflakeSupport the show (https://www.patreon.com/datacouture)

Show: 382Description: Brian talks with Sam Charrington (@samcharrington, Machine Learning & AI analyst, advisor & host of “This Week in Machine Learning & AI” podcast) about trends in the industry, the evolution of AI at the edge, new research areas in 2019, and a discussion about adding AI and ML to business applications. Show Sponsor Links:Liquid Technology - IT Value RecoveryTry CloudLast Service, get a free t-shirt and chance at Amazon Gift CardDatadog Homepage - Modern Monitoring and AnalyticsTry Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirtShow Interview Links:This Week in Machine Learning & AI Homepage - http://twimlai.comKubernetes for Machine Learning, Deep Learning and AI (eBook) - https://twimlai.com/kubernetes/Sam Charrington on Eps.321 of The Cloudcast - http://www.thecloudcast.net/2017/11/the-cloudcast-321-understanding-ai-and.htmlShow Notes:Topic 1 - Happy New Year and welcome back to the show, it’s been just over a year. For those that didn’t hear that show or might be new to TWIML & AI, tell us about your background and some of your AI/ML focus now.Topic 2 - Let’s start with the things that are considered “mainstream” with AI & ML today. Fraud detection, recommendation engines, facial recognition, speech recognition, auto-completions. What’s missing from that list, and how “commodity” have those technologies, tools, datasets, cloud services become?Topic 3 -On the flipside, what are some of the areas where research or just the massive cloud providers are focused today?Topic 4 - A couple years ago it seemed like TWIML & AI was a mix of technology discussions and business/social impacts. This past year seemed to be a deeper focus on the underlying technologies. What’s the current state of the balance between AI & ML for computing improvement vs. concerns about personal privacy, etc.?Topic 5 - What’s the “getting started” curve look like for companies that want/need to add or integrate AI & ML into their applications? What are some numbers you hear about cost of engineers, sizes of datasets, number of experiments and models needed to run, etc.?Topic 6 - What are some of the things you’re really looking forward to in 2019, whether it’s technology or trends or something else?Feedback?Email: show at thecloudcast dot netTwitter: @thecloudcastnet and @ServerlessCast

Joining us this week is Mathew Lodge, SVP of Products & Marketing of Anaconda. Highlights • 2 min 57 sec: What does Anaconda do? o Help data scientists be productive & enterprise AI / Data Science • 3 min 36 sec: How do you interact with Anaconda? o About 2.5 million downloads a month of Anaconda Distribution o Install binary packages for data science to Python • 5 min 55 sec: Who are data scientists? o Data wrangling and understanding • 9 min 12 sec: Data Science as a verb o Understand how to turn data into actionable insight • 10 min 47 sec: How learn to use the tools? Community! o Community around Anaconda open source to share packages, etc • 13 min 26 sec: How does Anaconda change as AI/Machine Learning improve? o Python is standard language with R close behind for data science • 14 min 58 sec: Reproducibility in results o 16 min 01 sec: Model training issue? • 17 min 16 sec: Parking lot on Sam Charrington’s AI Bias Podcasts o TWiML & AI - https://twimlai.com/ • 17 min 43 sec: Training models for limited sets of data for reliability in Edge o Answer by example of Google ImageNet o 20 min 14 sec: Optimizations to reduce processing requirements  Hey Siri example on how iPhone works o 22 min 03 sec: Do models improve over time? Transfer learning • 22 min 30 sec: Accelerative Learning in AI o Fashion example of layering learning o Issues around lack of data for training • 26 min 01 sec: Portability of models via Anaconda • 26 min 48 sec: Cloud Native Model of AI (no longer 2004) o Moved on from Java and distributed computing to Kubernetes o 29 min 05 sec: Giving up data locality (Hadoop) & specialized hardware? o 32 min 42 sec: Cloud model gives private and public options • 34 min 23 sec: How Anaconda play into the Cloud Native data science model? o Data scientists interested in data problems not cloud architecture o Data science as a Service o Kubernetes & Docker installed for you by Anaconda • 38 min 05 sec: WRAP UP o Anaconda Con Videos

In this episode, we talk with Sam Charrington of Cloud Pulse on the trends shaping AI adoption in the enterprise. Sam also hosts a popular podcast called "This Week in ML and AI" --- Support this podcast: https://anchor.fm/modernenterprise/support

Aaron & Brian talk with Sam Charrington (@samcharrington, Host of This Week in ML & AI Podcast) about the differences between AI and ML, how to manage data gravity, the maturity of the technology, press coverage, and the societal impacts of AI and ML. Show Links: This Week in AI & ML Podcast CloudPulse Strategies [PODCAST] @PodCTL - Containers | Kubernetes - RSS Feed, iTunes, Google Play, Stitcher, TuneIn and all your favorite podcast players [A CLOUD GURU] Get The Cloudcast Alexa Skill [A CLOUD GURU] DISCOUNT: Serverless for Beginners (only $15 instead of $29) [A CLOUD GURU] FREE: Alexa Development for Absolute Beginners [FREE] eBook from O'Reilly Show Notes Topic 1 - Welcome to the show. We’ve known you for a while as being heavily involved in Cloud since the early days, but you’ve been involved with AI and ML for quite a while now too. Tell our audience about your background and what you’re up to today. Topic 2 - Is there a difference between AI and ML? What are some good examples of each? Topic 3 - Let’s start with the basics. AI and ML always get talked about as a spectrum between basic things we all live with (like Google doing an auto fill on a search) to the scary SkyNet, Terminator stuff. Where are we on the maturity curve of AI and ML? Topic 4 - What are some of the key technology elements that people should be aware of with AI and ML? Do we make a mistake by mentioning them today (e.g. AI and ML), and are they very different? Topic 5 - Can we talk about data and data models/set and data gravity as it relates to AI and ML? Do you bring the data to the engine, or the engine to the data? How do companies deal with this today? Topic 6 - AI and ML also have the ability to have major societal impacts on our work, from jobs to human privacy to better access to healthcare or studying global warming. Can you talk about how this interaction of technology and human interests is being covered in the press & media? Topic 7 - Beyond listening to your show each week, what are some good resources for listeners to go learn about AI and ML? Feedback? Email: show at thecloudcast dot net Twitter: @thecloudcastnet and @ServerlessCast

The state of Docker and the container ecosystem was the touchstone for discussion at The New Stack's pancake breakfast hosted by Alex Willams at ContainerCon 2015. Joining Alex were Krishnan Subramanian, Director of OpenShift Strategy at Red Hat, Aneel Lakhani from the marketing team at SignalFx, Erica Windisch, a security engineer at Docker, and Sam Charrington, analyst with The New Stack. Watch on YouTube: https://youtu.be/WOs9eZOxAJI Learn more at: https://thenewstack.io/tns-analysts-show-56-the-pancake-breakfast-circuit-comes-to-containercon/

A lot of great thinkers and makers are all in one place at a conference such as OSCON. For this edition of The New Stack Analysts podcast, Alex Williams gathered several such people at one time, in what he suspects is the largest group he's assembled yet for a podcast, and in this case bigger is definitely better. The panel: Sam Charrington, principal analyst with The New Stack Casey West, principal technologist for Cloud Foundry at Pivotal Jesse Proudman, CTO at Blue Box, An IBM Company Abby Kearns, Technical Marketing for Pivotal Cloud Foundry Sam Ramji, CEO Cloud Foundry Foundation Klint Finley, Journalist for Wired, The New Stack and Mindful Cyborgs Watch on YouTube: https://www.youtube.com/watch?v=v8ltFnrTCTY Learn more at: https://thenewstack.io/tns-analysts-show-54-live-at-oscon-cloud-native-before-and-after-containers/

We’ve got two great interviews this week, focusing on some emerging technologies. First, Will Young (@zapposlabs), the director of Zappos Labs, stops by to talk about the customer experience and using analytics and social media to find new ways for customers to shop and interact. Then Sam Charrington (@samcharrington), the principal of CloudPulse Strategies (and co-founder of Cloudcamp with Ruv), joins DNP to break down IaaS, PaaS, and SaaS and to discuss where PaaS is heading in the enterprise. Show Timeline: • 0:00 – Introductions and News of the Week • 10:18 – Interview with Will Young • 22:52 – Interview with Sam Charrington • 39:03 – Wrap up