MLOps.community

MLOps Coffee Sessions #128 with Simon Thompson, Managing Machine Learning Projects co-hosted by Abi Aryan. // Abstract It's a cliche to say that choosing and running the algorithms is only a small part of a typical ML project but despite that it's true! Setting up and organizing the project, dealing with the data asset, getting to the heart of the business problem, assessing and choosing the models, and integrating them with the business processes in production are all at least as time-consuming and important.    Simon has written a book that talks about how these different activities need to be orchestrated and executed and he hopes that it might be useful for people who are starting out managing ML projects and help them avoid some of the crunches and catches that seem to trip people up. // Bio Simon has been building and running ML projects since 1994 (when he started his Ph.D. in MachineLearning). His first commercial project was for the Royal Navy, and since then he has worked in Telecom, Defense, Consultancy, Manufacturing, and Finance. This means Simon has experienced a wide range of working environments and different types of projects. As well as working in a variety of commercial environments Simon collaborated on EU research projects, UK Government funded research projects and worked as an industrial rep on three MIT consortia (BigData@CSAIL, Systems That Learn, and the CISR Data Research Board). Simon was also an industrial fellow at the Alan Turing Institute for a year. This means that he has also seen a lot of the communities' practices and concerns as they developed, and he had the chance to put them into use in a commercial environment.    Right now, Simon is working for a technology consultancy called GFT, and his job there is primarily to deliver ML projects for companies in the capital markets such as investment banks, although we also do work in retail banking, insurance, and manufacturing. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://medium.com/@sgt101 Managing Machine Learning Projects From design to deployment book by Simon Thompson: https://www.manning.com/books/managing-machine-learning-projects MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3Y Language processing. Simon Thompson CO545 Lecture 10: https://docplayer.net/211236676-Language-processing-simon-thompson-co545-lecture-10.html --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/ Connect with Simon on LinkedIn: https://www.linkedin.com/in/simon-thompson-025a7/

MLOps Coffee Sessions #127 with Niall Murphy & Todd Underwood, Reliable ML co-hosted by David Aponte. // Abstract By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. (Book description from O'Reilly) MLOps Coffee Sessions #127 with Niall Murphy & Todd Underwood, Reliable ML co-hosted by David Aponte.   // Abstract By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. (Book description from O'Reilly)  It was great that they wrote this book in the first place in a space that's new and lots of people are entering it with a lot of questions and this book clarifies those questions. It was also great to have all of their experiences documented in this one book and there's a lot of value in putting them all in one place so that people can benefit from it. // Bio Niall Murphy Niall has been interested in Internet infrastructure since the mid-1990s. He has worked with all of the major cloud providers from their Dublin, Ireland offices - most recently at Microsoft, where he was the global head of Azure Site Reliability Engineering (SRE). His books have sold approximately a quarter of a million copies worldwide, most notably the award-winning Site Reliability Engineering, and he is probably one of the few people in the world to hold degrees in Computer Science, Mathematics, and Poetry Studies. He lives in Dublin, Ireland, with his wife and two children. Todd Underwood Todd is a Director at Google and leads Machine Learning for Site Reliability Engineering Director. He is also the Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google.     Before working at Google, Todd held a variety of roles at Renesys.  He was in charge of operations, security, and peering for Renesys’s Internet intelligence services which are now part of Oracle's Cloud service. He also did product work for some early social products that Renesys worked on. Before that Todd was the Chief Technology Officer of Oso Grande, an independent Internet service provider (AS2901) in New Mexico. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Reliable Machine Learning book by Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood: https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/ --------------- ✌️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/ Connect with Niall on LinkedIn: https://www.linkedin.com/in/niallm/ Connect with Todd on LinkedIn: https://www.linkedin.com/in/toddunder/

MLOps Coffee Sessions #126 with George Mathew, ML Unicorn Start-up Investor Tells-IT-All. // Abstract What's so enticing about enterprise software? It's incredible to see George's idea and vision to invest in generationally enduring companies.   Let's look at the way how George likes to structure deals with companies while he's reviewing them and let's look at the MLOps ecosystem through the eyes of the investors. // Bio George Mathew joins Insight Partners as a Managing Director focused on venture stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit.   He brings 20+ years of experience developing high-growth technology startups including most recently being CEO of Kespry. Prior to Kespry, George was President & COO of Alteryx where he scaled the company through it’s IPO (AYX). Previously he held senior leadership positions at SAP and salesforce.com. He has driven company strategy, led product management and development, and built sales and marketing teams.     George holds a Bachelor of Science in Neurobiology from Cornell University and a Masters in Business Administration from Duke University, where he was a Fuqua Scholar. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.insightpartners.com/ --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with George on LinkedIn: https://www.linkedin.com/in/gmathew/

MLOps Coffee Sessions #125 with Rafael Pierre, Deploying Real-time ML Models in Minutes with Databricks Model Serving V2 co-hosted by Ryan Russon. // Abstract From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models.   With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀). // Bio Rafael has worked for 15 years in data-intensive fields within finance in multiple roles: software engineering, product management, data engineering, data science, and machine learning engineering.    At Databricks, Rafael has fun bringing all these topics together as a Solutions Architect to help our customers become more and more data-driven. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://mlopshowto.com Airflow Summit 2022: https://youtu.be/JsYEOdRBgREING Data Engineering Meetup: https://www.youtube.com/watch?v=gJoxX1rRZJI MLOps World Virtual Summit NYC 2022: https://drive.google.com/file/d/1EXsqmLfrPAsV9i6h6pGfJxVjMO9y6u9a/view?usp=sharing --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryanrusson/ Connect with Rafael on LinkedIn: https://www.linkedin.com/in/rafaelpierre

Lightning Sessions #2 with Aparna Dhinakaran, Co-Founder and Chief Product Officer, and Jason Lopatecki, CEO and Co-Founder of Arize. Lightning Sessions is sponsored by Arize // Abstract   Monitoring embeddings on unstructured data is not an easy feat let's be honest. Most of us know what it is but don't understand it one hundred percent.   Thanks to Aparna and Jason of Arize for breaking down embedding so clearly. At the end of this Lightning talk, we get to see a demo of how Arize deals with unstructured data and how you can use Arize to combat that. // Bio Aparna Dhinakaran Aparna is the Co-Founder and Chief Product Officer at Arize AI, a pioneer, and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michaelangelo. Aparna has a BA from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University. Jason Lopatecki Jason is the Co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe.    Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links https://arize.com/ --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aparna on LinkedIn: https://www.linkedin.com/in/aparnadhinakaran/ Connect with Jason on LinkedIn: https://www.linkedin.com/in/jason-lopatecki-9509941/ Timestamps: [00:00] Introduction to the topic [01:13] Troubleshooting unstructured ML models is difficult [01:40] Challenges with monitoring unstructured data [02:10] How data looks like [03:02] Embeddings are the backbone of unstructured models [03:28] ML teams need a common tool [04:06] What are embeddings? [05:08] The real WHY behind AI [06:41] ML observability for unstructured data [07:08] Index and Monitor every Embedding [08:05] Measuring drift of unstructured data [08:54] Interactive visualizations   [09:34] Fix underlying data issue [09:44] Data-centric AI workflow [10:08] Demo of the product [12:48] Wrap up

MLOps Coffee Sessions #124 with Kush Varshney, Distinguished Research Staff Member and Manager IBM Research, Trustworthy Machine Learning co-hosted by Krishnaram Kenthapadi. // Abstract Trustworthy ML is a way of thinking and something to be worked on and operationalized throughout the entire machine learning development lifecycle, starting from the problem specification phase that should include diverse stakeholders. // Bio Kush R. Varshney was born in Syracuse, New York in 1982. He received a B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, New York, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow. Dr. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the machine learning group in the Foundations of Trustworthy AI department. He was a visiting scientist at IBM Research - Africa, Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences and the 2019 Computing Community Consortium / Schmidt Futures Computer Science for Social Good White Paper Competition. He self-published a book entitled 'Trustworthy Machine Learning in 2022, available at http://www.trustworthymachinelearning.com. He is a senior member of the IEEE. // 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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Krishnaram on LinkedIn: https://www.linkedin.com/in/krishnaramkenthapadi Connect with Kush on LinkedIn: https://www.linkedin.com/in/kushvarshney/

MLOps Coffee Sessions #123 with Gleb Abroskin, Machine Learning Engineer at Funcorp, RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day co-hosted by Jake Noble. // Abstract FunCorp was a top 10 app store. It was a very popular app that has a ton of downloads and just memes. They need a recommendation system on top of that. Memes are super tricky because they're user-generated and they evolve very quickly. They're going to live and die by the Recommender System in that product. It's incredible to see FunCorp's maturity. Gleb breaks down the feature store they created and the velocity they have to be able to create a whole new pipeline in a new model and put it into production after only a month! // Bio Gleb make models go brrrrr. He doesn't know what is expected in this field, to be honest, but Gleb has experience in deploying a lot of different ML models for CV, speech recognition, and RecSys in a variety of languages (C++, Python, Kotlin) serving millions of users worldwide. / MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Putting a two-layered recommendation system into production - https://medium.com/@FunCorp/putting-a-two-layered-recommendation-system-into-production-b8caaf61393d Practical Guide to Create a Two-Layered Recommendation System - https://medium.com/@FunCorp/practical-guide-to-create-a-two-layered-recommendation-system-5486b42f9f63 Ten Mistakes to Avoid When Creating a Recommendation System - https://medium.com/@FunCorp/ten-mistakes-to-avoid-when-creating-a-recommendation-system-8268ed60aeba Applying Domain-Driven Design And Patterns: With Examples in C# and .net 1st Edition by Jimmy Nilsson: https://www.amazon.com/Applying-Domain-Driven-Design-Patterns-Examples/dp/0321268202 --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jake on LinkedIn: https://www.linkedin.com/in/jakednoble/ Connect with Gleb on LinkedIn: https://www.linkedin.com/in/gasabr/ Timestamps: [00:00] Introduction to Gleb Abroskin [00:50] Takeaways [05:39] Breakdown of FunCorp teams [06:47] FunCorp's team ratio [07:41] FunCorp team provisions [08:48] Feature Store vision [10:16] Matrix factorization [11:51] Fairly modular fairly thin infrastructure [12:26] Distinct models with the same feature [13:08] FunCorp's definition of Feature Store [15:10] Unified API [15:55] FunCorp's scaling direction [17:01] Level up as needed [17:38] Future of FunCorp's Feature Store [18:37] Monitoring investment in the space [19:43] Latency for business metrics [21:04] Velocity to production [23:10] 30-day retention struggle [24:45] Back-end business stability [27:49] Recommender systems [30:34] Back-end layer headaches [32:04] Missing piece of the whole Feature Store picture [33:54] Throwing ideas turn around time [36:37] Decrease time to market [37:41] Continuous training pipelines or produce an artifact [39:33] Worst-case scenario [40:38] Realistic estimation of a new model deployment [41:42] Recommender Systems' future velocity   [43:07] A/B Testing launch - no launch decision [46:32] Lightning question [47:08] Wrap up

MLOps Coffee Sessions #122 with Hannes Hapke, Machine Learning Engineer at Digits Financial, Inc., Scaling Similarity Learning at Digits co-hosted by Vishnu Rachakonda. // Abstract Machine Learning in a product is a double-edged sword. It can make a product more useful but it depends on assumed and strictly defined behavior from users.   Hannes walks through the entirety of their machine learning pipeline, how they implemented it, what the elements are, what the learning looks like, and what tooling looks like.    Hannes maps out what good data hygiene looks like not only from the machine learning perspective down to the software engineering, design, and backend engineering, all the way to the data engineering perspectives. // Bio Hannes was the first ML engineer at Digits, where he built the MLOPs foundation for their ML team. His interest in production machine learning ranges from building ML pipelines to scaling similarity-based ML to process millions of banking transactions daily.    Prior to Digits, Hannes implemented ML solutions for a number of applications, incl. retail, health care, or ERP companies. He co-author two machine learning books: * Building Machine Learning Pipeline (O'Reilly) * NLP in Action (Manning) // 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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Hannes on LinkedIn: https://www.linkedin.com/in/hanneshapke/ Timestamps: [00:00] Introduction to Hannes Hapke [01:37] Takeaways [02:40] Design supercharges machine learning [05:48] Building Machine Learning Pipeline book [08:09] Updating the edition [09:37] Abstract away [11:52] Approach of crossover [16:04] Training serving skew [20:42] Tools using continuous integration and deployment [25:25] Human in the loop touch point [27:44] Data backfilling update [30:06] Work and Products of Digits [32:26] Digit Boost [35:30] The first machine learning engineer [39:55] Structured data in good shape, good data processing perspective, concept-educated teams   [43:33] Digits is hiring! [43:55] Machine Learning struggles [47:10] Design decision [49:49] Data or machine learning literacy [51:30] Data Hygiene [52:49] Rapid fire questions [54:47] Wrap up

MLOps Coffee Sessions #121 with Luis Ceze, CEO and Co-founder of OctoML, Bringing DevOps Agility to ML co-hosted by Mihail Eric.   // Abstract There's something about this idea where people see a future where you don't need to think about infrastructure. You should just be able to do what you do and infrastructure happens.   People understand that there is a lot of complexity underneath the hood and most data scientists or machine learning engineers start deploying things and shouldn't have to worry about the most efficient way of doing this. // Bio Luis Ceze is Co-Founder and CEO of OctoML, which enables businesses to seamlessly deploy ML models to production making the most out of the hardware. OctoML is backed by Tiger Global, Addition, Amplify Partners, and Madrona Venture Group. Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has taught for 15 years. Luis co-directs the Systems and Architectures for Machine Learning lab (sampl.ai), which co-authored Apache TVM, a leading open-source ML stack for performance and portability that is used in widely deployed AI applications.    Luis is also co-director of the Molecular Information Systems Lab (misl.bio), which led pioneering research in the intersection of computing and biology for IT applications such as DNA data storage. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, and the Wall Street Journal. Ceze is a Venture Partner at Madrona Venture Group and leads their technical advisory board. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Landing page: https://octoml.ai/ The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics by Daniel James Brown: https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478 --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Connect with Luis on LinkedIn: https://www.linkedin.com/in/luis-ceze-50b2314/ Timestamps: [00:00] Introduction to Luis Ceze [06:28] MLOps does not exist [10:41] Semantics argument [16:25] Parallel programming standpoint [18:09] TVM [22:51] Optimizations [24:18] TVM in the ecosystem [27:10] OctoML's further step [30:42] Value chain [33:58] Mature players [35:48] Talking to SRE's and Machine Learning Engineers [36:32] Building OctoML [40:20] My Octopus Teacher [42:15] Environmental effects of Sustainable Machine Learning [44:50] Bridging the gap from OctoML to biological mechanisms [50:02] Programmability [57:13] Academia making the impact [59:40] Rapid fire questions [1:03:39] Wrap up

MLOps Coffee Sessions #120 with David Stein, Senior Staff Software Engineer at LinkedIn, Feathr: LinkedIn's Enterprise-Grade, High-Performance Feature Store co-hosted by Skylar Payne. // Abstract When David started building Feathr, Feature Stores did not exist. That was not a term floating around at all. This was definitely one of the OG Feature Stores for sure!   We hear how the LinkedIn team got to this point, having an open source release, and how they used LinkedIn as an incubator to build a great product. // Bio David Stein is a tech lead at LinkedIn working on machine learning feature infrastructure. He is the original architect of Feathr and continues to contribute to its development as well as to other parts of LinkedIn's machine learning platform. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://engineering.linkedin.com/blog/2022/open-sourcing-feathr---linkedin-s-feature-store-for-productive-m * https://github.com/linkedin/feathr * https://engineering.linkedin.com/blog/2022/open-sourcing-feathr---linkedin-s-feature-store-for-productive-m * https://azure.microsoft.com/en-us/blog/feathr-linkedin-s-feature-store-is-now-available-on-azure/ --------------- ✌️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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/ Connect with David on LinkedIn: https://www.linkedin.com/in/steindavidj/ Timestamps: [00:00] Introduction to David Stein [00:30] Takeaways [06:58] David and Skylar's reunion [08:34] Feathr's background [12:04] Lessons learned in building Feathr [16:35] Scale of Feathr [20:25] Systems interaction [24:15] Standardization [29:20] Importance and better difference of Feathr [34:30] Feature Stores' evolution and more generally MLOps [37:57] Challenges in real-time [40:09] Going real-time, you're not ready! [42:24] Use cases strong for real-time unleveraged now [45:50] Proud about [47:58] Going back in time [50:47] Feathr as a separate company? [52:53] LinkedIn is hiring!