MLOps Coffee Sessions #132 {Podcast BTS} with Ian Schweer, What is Data / ML Like on League? co-hosted by Skylar Payne.   // Abstract If you're not an avid gamer yourself, you have never really seen how ML might be used in the gaming space. It's so interesting to see the things that are different like full stories of players' games from start to finish.   // Bio On the surface, Ian is an excellent developer who gets things done. Underneath, he is much more. Ian is a reliable and trustworthy teammate who demonstrates an exceptional ownership mentality.   Here's a fair share of Ian's job history: 2014 - UCI (With Skylar!) 2015 - Adobe Primetime (SWE) 2017 - Adobe Product and Customer Analytics (SWE) 2019 - Doordash Data Infra (SWE) Current - Riot Games on League   // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Landing page: https://www.riotgames.com/en --------------- ✌️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 Ian on LinkedIn: https://www.linkedin.com/in/ianschweer/ Timestamps: [00:00] Ian's preferred coffee [02:10] Takeaways [05:14] Please hit the like button and leave us a review. Please subscribe also! [05:45] Engineering Community Mental Health Awareness [07:33] Coping mechanism [09:29] Increase in video game playing   [11:20] Ian's career progression [17:55] Lessons to apply in the Data space [24:23] Challenges at Riot [34:18] Real-time element [39:09] Ian's day-to-day responsibilities [43:13] Analysis vs. Production Code Quality [48:11] Tools and techniques on the reality of writing production codes [55:00] What would you change your career into? [57:00] Ian's best practices advise [58:28] Ian's favorite video game [59:58] Wrap-up

MLOps Coffee Sessions #131 {Podcast BTS} with Ethan Rosenthal, Let's Continue Bundling into the Database co-hosted by Mike Del Balso. // Abstract The relationship between ML Engineers and Product Managers is something that we don't talk about enough. We've got to get this right. If we don't get this right, either you're not focusing on the business problems in the right way or the Product Managers are not going to understand the tech appropriately to help make the right decisions. // Bio Ethan works on the Conversations Team at Square leading a team of Artificial Intelligence Engineers. Ethan's team builds applied AI solutions for Square Messages, a messaging hub for Square merchants to communicate with their customers. Prior to Square, Ethan spent time as a freelance data science consultant building machine learning products for a range of companies, from pre-seed startups to Fortune 100 enterprises.    Ethan got his start in data science working at two different e-commerce startups, Birchbox and Dia&Co. Before data science, Ethan was an actual scientist and got his Ph.D. in experimental physics from Columbia University. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.ethanrosenthal.com/ Relevant blog posts:   https://www.ethanrosenthal.com/2022/05/10/database-bundling/ https://www.ethanrosenthal.com/2022/07/19/materialize-ml-monitoring/ https://www.ethanrosenthal.com/2022/01/18/autoretraining-is-easy/ --------------- ✌️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 Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/ Connect with Ethan on LinkedIn: https://www.linkedin.com/in/ethanrosenthal/ Timestamps: [00:00] Ethan's preferred coffee [00:10] Introduction to co-host Mike Del Balso [00:43] Takeaways [04:10] Sign up for our newsletter! [04:47] Ethan's team [06:49] Ethan's team improvement [08:40] Product manager role at Square [10:39] Large Language Models [12:22] Big questions to figure out [15:45] Cost of false-positive [18:20] Company Vocabulary [20:11] MLOps concerns and challenges around Large Language Models [23:42] Data learning management [27:36] Leveling trade-offs [30:25] Ethan's Database Bundling blog [34:32] Feature Stores [38:24] Streaming databases [41:57] Best of both worlds trade-off highlight [43:51] Rosenthal data [46:40] Ethan's freelancing [47:46] Risk-reward trade-off [49:17] Ethan as a professor [51:14] Wrap up

MLOps Coffee Sessions #130 {Podcast BTS} with Andrew Yates, Adversarial MLOps on Other People's Money: Lessons Learned from Ad Tech co-hosted by Abi Aryan. // Abstract Design ML to be components in a larger system with stable interfaces is not tracible to monitor the entire stack as a black box. You need intermediate ground-truth signals. Ads are designed in this way. You can go from profitable to non-profitable real quick with ads. This will determine whether your company is around a year or two. You play with money and sometimes you play a lot of it so make sure that it's correct. // Bio Andrew Yates formerly led ads ranking, auction, and marketplace engineering and research teams at Facebook and Pinterest. He specializes in designing billion-dollar content marketplaces that maximize long-term revenue while protecting both seller and user experiences. Andrew has published over a dozen patents in online advertising optimization. // 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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/ Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-yates-0217a985/

MLOps Coffee Sessions #129 {Podcast BTS} with Catherin Breslin, Voice and Language Tech co-hosted by Adam Sroka. // Abstract Back in the day, Speech Recognition was its own thing. It's a very different flavor of Data Science. You could not use a lot of the tools. It wouldn't cross over to this type of machine learning. Now, with the advancements, Speech Recognition and Machine learning are coming in together. It's interesting to hear right from someone with a Ph.D. level working with some of the biggest companies in the world doing it. The fact that something like Alexa is lots of models back to back and just fathom the complexity of that is quite cool! // Bio Catherine is a machine learning scientist and consultant based in Cambridge UK, and the founder of Kingfisher Labs consulting. Since completing her Ph.D. at the University of Cambridge in 2008, Catherine has commercial and academic experience in automatic speech recognition, natural language understanding, and human-computer dialogue systems, having previously worked at Cambridge University, Toshiba Research, Amazon Alexa, and Cobalt Speech. Catherine has been excited by the application of research to real-world problems involving speech and language at scale. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links www.catherinebreslin.co.uk https://catherinebreslin.medium.com/ MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3YTwitter: https://twitter.com/catherinebuk --------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka Connect with Catherine on LinkedIn: https://www.linkedin.com/in/catherine-breslin-0592423a/ Timestamps: [00:00] Catherine's preferred coffee [01:50] Takeaways [03:59] Introduction to Catherine Breslin [05:04] Subscribe to our newsletter! [06:25] Catherine's background [08:13] Speech Recognition trajectory [09:36] Challenges around technologies and tools [11:34] Reflective trend [13:02] Developer experiences hiccups [15:09] Speech Recognition use case backup [16:56] Toshiba research [17:48] Transition from a research lab to working in the industry [20:01] Unit test of Speech Recognition [20:56] Alexa [22:33] Maturity process of Speech Recognition [26:48] Speech Recognition unrecognizing challenges [30:38] Mechanical Terk [33:00] Social media listening [34:05] Pipeline models and speed of Speech Recognition [36:48] Development of Speech Recognition excited about [37:23] Data from people for the Speech Recognition system vs Scowering news vs watching Youtube for a long time [40:00] Disappearing Languages [41:30] Future of an online practice partner [43:17] Speech-to-speech translation [44:04] Interesting ways to use unfamiliar models to achieve a result [45:40] Meeting transcriptions [48:37] First toy problem of a new Speech Recognition learner [51:37] Kingfisher Labs' problems to tackle [52:18] Off-the-shelf solution [53:38] Translation layer [54:15] Connect with Catherine on Twitter and LinkedIn for available jobs [54:43] Wrap up

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/