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MLOps Coffee Sessions #139 with Alex Ratner, Putting Foundation Models to Use for the Enterprise, co-hosted by Abi Aryan, sponsored by Snorkel AI.// AbstractFoundation models are rightfully being compared to other game-changing industrial advances like steam engines or electric motors. They’re core to the transition of AI from a bespoke, less predictable science to an industrialized, democratized practice. Before they can achieve this impact, however, we need to bridge the cost, quality, and control gaps.   Snorkel Flow Foundation Model Suite is the fastest way for AI/ML teams to put foundation models to use. For some projects, this means fine-tuning a foundation model for production dramatically faster by creating programmatically labeling training data. For others, the optimal solution will be using Snorkel Flow’s distill, combine, and correct approach to extract the most relevant knowledge from foundation models and encode that value into the right-sized models for your use case.   AI/ML teams can determine which Foundation Model Suite capabilities to use (and in what combination) to optimize for cost, quality, and control using Snorkel Flow’s integrated workflow for programmatic labeling, model training, and rapid-guided iteration.// BioAlex Ratner is the Co-founder and CEO of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington.   Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open-source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: www.snorkel.aiHuge “foundation models” are turbo-charging AI progress: https://www.economist.com/interactive/briefing/2022/06/11/huge-foundation-models-are-turbo-charging-ai-progressNemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming: https://arxiv.org/abs/2203.01382The Principles of Data-Centric AI Development: https://snorkel.ai/principles-of-data-centric-ai-development/--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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 Alex on LinkedIn: https://www.linkedin.com/in/alexander-ratner-038ba239/Timestamps: [00:00] Alex's preferred coffee [01:20] Introduction to Alex Ratner [02:34] Takeaways [04:04] Huge shoutout to our Sponsor, Snorkel AI! [04:39] Comment, rate us, and share our podcasts with your friends!  [04:50] Transfer Learning / Active Learning [11:30] Labeling Heuristics paper on Nemo [18:14] Geocentric AI [21:48] Enterprise use cases on Foundational Models [32:45] Foundational Models in the different Google products [38:36] Progress in Foundational Models [43:55] AutoML Models Baseline Accuracy [44:40] Hosting Infrastructure Snorkel Float vs GCP [46:53] Chris Re's venture capital firm/incubator/machine [51:00] Wrap

MLOps Coffee Sessions #138 with Dattaraj Rao, Explainability in the MLOps Cycle, co-hosted by Vishnu Rachakonda.// AbstractWhen it comes to Dattaraj's interests, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into, and where he invests his own time.Learn more about rule-based systems, deploying rule-based systems, and how to incorporate systems into more systems. There is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule-based models.// BioDattaraj Jagdish Rao is the author of the book “Keras to Kubernetes: The Journey of a Machine Learning Model to Production”. Dattaraj leads the AI Research Lab at Persistent and is responsible for driving thought leadership in AI/ML across the company. He leads a team that explores state-of-the-art algorithms in Knowledge Graphs, NLU, Responsible AI, MLOps, and demonstrates applicability in Healthcare, Banking, and Industrial domains. Earlier, he worked at General Electric (GE) for 19 years, building Industrial IoT solutions for Predictive Maintenance, Digital Twins, and Machine Vision.Dattaraj held several Technology Leadership roles at Global Research, GE Power, and Transportation (now part of Wabtec). He led the Innovation team out of Bangalore that incubated video track inspection from an idea into a commercial Product. Dattaraj has 11 patents in the Machine Learning and Computer Vision areas.// MLOps Jobs boardjobs.mlops.community  // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksKeras to Kubernetes: The Journey of a Machine Learning Model to Production book:https://www.amazon.com/Keras-Kubernetes-Journey-Learning-Production/dp/1119564832Responsible Data Science Research | Talk @ VLDB 2022| Dattaraj Raohttps://www.youtube.com/watch?v=5_19KvSiy8sOperationalizing AI/ML: Journey of an ML Model to Production | Masterclass by Dattaraj Raohttps://www.youtube.com/watch?v=Zk3RiiG07UsDattaraj Rao presenting workshop on MLOps at VISUM 2021https://www.youtube.com/watch?v=wonUvbMDTUAMachine Learning Design Patterns book: https://www.oreilly.com/library/view/machine-learning-design/9781098115777/--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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 Dattaraj on LinkedIn: https://www.linkedin.com/in/dattarajrao/Timestamps:[00:00] Dattaraj's preferred coffee[01:12] Introduction to Dattaraj Rao[02:57] Takeaways[05:10] This podcast is brought to you by Superwise![06:10] Dattaraj's background[12:37] Top 3 interests of Dattaraj[16:23] Examples of Large Language Models use cases are not a good application[21:44] Future of Large Language Models - change or inherent problem[23:12] Remote Monitoring and Diagnostic[29:25] Keras to Kubernetes book[33:44] Dattaraj's title for his next book[37:12] Machine Learning Design Patterns to keep in mind[43:10] Model registries and multi-tenancy[44:49] Wrap up

MLOps Coffee Sessions #137 with Niklas Kühl, Machine Learning Operations — What is it and Why Do We Need It? co-hosted by Abi Aryan.// Abstract The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production.   However, it is highly challenging to automate and operationalize ML products, and thus, many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue.// Bio NIKLAS KÜHL studied Industrial Engineering & Management at the Karlsruhe Institute of Technology (KIT) (Bachelor's and Master's). During his studies, he gained practical experience in IT by working at Porsche in both national and international roles. Niklas has been working on machine learning (ML) and artificial intelligence (AI) in different domains since 2014. In 2017, he gained his PhD (summa cum laude) in Information Systems with a focus on applied machine learning from KIT. In 2020, he joined IBM.As of today, Niklas engages in two complementary roles: He is head of the Applied AI in Services Lab at the Karlsruhe Institute of Technology (KIT), and, furthermore, he works as a Managing Consultant for Data Science at IBM. In his academic and practical projects, he is working on conceptualizing, designing, and implementing AI in Systems with a focus on robust and fair AI as well as the effective collaboration between users and intelligent agents. Currently, he and his team are actively working on different ML & AI solutions within industrial services, sales forecasting, production lines or even creativity. Niklas is internationally collaborating with multiple institutions, like the University of Texas and the MIT-IBM Watson AI Lab.// MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/// Related Links Website: niklas.xyz MLOps Newsletters: https://airtable.com/shrx9X19pGTWa7U3YMachine Learning Operations (MLOps): Overview, Definition, and Architecture paper: https://arxiv.org/abs/2205.02302--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch 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 Niklas on LinkedIn: https://www.linkedin.com/in/niklaskuehl/Timestamps: [00:00] Niklas' preferred coffee [00:43] Introduction to Niklas Kühl [01:16] Takeaways [02:05] Subscribe to our newsletters and give us a rating here! [02:54] Niklas background [05:09] Scraping Twitter data [06:58] EV's conclusions [08:24] NLP usage on Twitter [10:26] Consumer behavior production [12:03] Management and Machine Learning Systems Communication [14:00] Current hype around Machine Learning [15:10] Budgeting ML Productions [18:15] Machine Learning Operations (MLOps): Overview, Definition, and Architecture paper [22:56] Niklas' MLOps definiton  [25:55] Navigating the idea of MLOps [30:34] Return on Investment endeavor [33:58] Full-stack data scientist [37:39] Defining success for different kinds of data science projects [41:06] Fun fact about Niklas [44:35] Other things Niklas does [47:02] The world is your oyster [50:57] Niklas' day-to-day life [52:48] One lecture Niklas can drop in [53:57] Foundational models [58:20] Wrap up

MLOps Coffee Sessions #136 with Andrew Dye, Systems Engineer, Navigating the World of ML, co-hosted by David Aponte.// AbstractWe don't hear that much about working at a very low level on this podcast, but they are still very valid. Andrew is able to give us his take on why and what you need to keep in mind when you are working at these low levels, and why it is very important when you are a Machine Learning Engineer, and how the two can play together nicely.Most MLOps teams are formed using existing people and existing engineers. More often than not, you have to blend these various disciplines, and it works well when there's a common goal.// BioAndrew is a software engineer at Union and a contributor to Flyte, a production-grade data and ML orchestration platform. Prior to that, he was a tech lead for ML Infrastructure at Meta, where he focused on ML training reliability.// MLOps Jobs boardjobs.mlops.community  // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrewwdyeTimestamps: [00:00] Andrew's preferred coffee [03:30] Introduction to Andrew Dye [03:33] Takeaways [07:32] Huge shoutout to our sponsors UnionML and UnionAI! [07:48] Andrew's background [10:08] Andrew's learning curve [11:10] Bridging the gap between firmware space and MLOps [12:18] In connection with the Pytorch team [12:54] Things that should have been learned sooner [14:54] Type of scale Andrew works on [17:42] Distributed training at Meta [19:55] Managing the huge search space [22:18] Execution patterns programs [23:20] Non-ML engineers dealing with ML engineers having the same skill set [26:44] Pace rapid change adoption [29:18] Consensus challenges [32:26] Abstractions making sense now [34:53] Comparing to others [39:21] General principles in UnionAI tooling [41:54] Seeing the future [43:54] Inter-task checkpointing [44:52] Combining functionality with use cases [46:17] Wrap up

MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference, co-hosted by Skylar Payne.  // AbstractMoving from batch/offline Machine Learning to more interactive "near" real-time requires knowledge, team, planning, and effort. We discuss what it means to do real-time inference and near-real-time features, when to make this move, what tools to use, and what steps to take.  // BioSasha Ovsankin Sasha is currently a Tech Lead of Machine Learning Model Serving infrastructure at LinkedIn, worked also on Feathr Feature Store, Real-Time Feature pipelines, designed metric platforms at LinkedIn and Uber, and was a co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many other things.  Rupesh GuptaRupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.  // MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links  --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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 Sasha on LinkedIn: https://www.linkedin.com/in/sashao/Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupeshTimestamps:[00:00] Sasha's and Rupesh's preferred coffee[01:30] Takeaways[07:23] Changes in LinkedIn[09:21] "Real-time" Machine Learning in LinkedIn[13:08] Value of Feedback[14:24] Technical details behind getting the most recent information integrated into the models[16:53] Embedding Vector Search action occurrence[18:33] Meaning of "Real-time" Features and Inference[20:23] Are "Real-time" Features always worth that effort and always helpful?[23:22] Importance of model application[25:26] Challenges in "Real-time" Features[30:40] System design review on Pinterest[36:13] Successes of real-time features[38:31] Learnings to share[45:52] Branching for Machine Learning[48:44] Not so talked about discussion of "Real-time"[51:09] Wrap up

MLOps Coffee Sessions #134 with Jeremy Thomas Jordan, Building Threat Detection Systems: An MLE's Perspective, co-hosted by Vishnu Rachakonda.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// AbstractThere is a clear pattern that we have been seeing with some of these greats in MLOps. So many use writing as a forcing function to learn about where they have holes in their understanding of something.   If you are not writing, this episode is important as to why writing is important for your own development. Jeremy goes into writing in depth as to how beneficial it is for him to write and for him to see that he doesn't understand something if he cannot re-articulate it in writing.// BioJeremy is a machine learning engineer currently working at Duo Security, where he focuses on building ML infrastructure to operate threat detection systems at scale. He previously worked at Proofpoint, where he built models for phishing and malware detection.// MLOps Jobs boardjobs.mlops.community  // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links Website:https://www.jeremyjordan.me/--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Visnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/Connect with Jeremy on Twitter: https://twitter.com/jeremyjordanTimestamps:[00:00] Jeremy's preferred coffee[00:15] Introduction of hosts[00:34] Introduction to Jeremy Thomas Jordan[01:11] Takeaways[04:50] Like, subscribe, and leave a review on our platforms[05:55] Normcore Conference[09:45] When are rule engines good and when are they not?[11:50] Going with Implemental Rule[12:13] Implementing a Rule[14:42] Managing rules sprawl[16:40] Reviewing and updating the rules[17:35] Chaining[19:27] Rules catching complicated threats[24:47] Notion of risk and confidence[26:38] Effective testing for machine learning systems blog post[28:51] Testing large language models[31:03] Model evaluation vs model testing[34:08] Approach to fundamentals [36:51] “The Cobbler’s Children Have No Shoes”[41:39] Writing is a checkpoint for Jeremy[43:10] Posting Jeremy's blogs on https://ghost.org/[44:32] Jeremy's blogs distribution platforms [46:18] Jeremy's most popular blog post[48:14] Sign up for NormConf for free here https://normconf.com/ and attend on December 15th! [49:50] Wrap up

MLOps Coffee Sessions #133 {Podcast BTS} with Chip Huyen, Real-time Machine Learning with Chip Huyen, co-hosted by Vishnu Rachakonda.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// Abstract Forcing functions and how you can supercharge your learning by putting yourself into a situation where you know you either have a responsibility to others to learn or accountability on you, so you have to learn.  It's not that hard when you think about streaming machine learning. It's not that big of a mental barrier to cross. It is simple in theory, but maybe it's more complicated in practice, and that's exactly where Chip's perspective is.// Bio Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Previously, she was with Snorkel AI and NVIDIA. She teaches CS 329S: Machine Learning Systems Design at Stanford. She’s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merch https://mlops-community.myshopify.com/// Related Links Landing page: https://claypot.ai Designing Machine Learning Systems book: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch 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 Chip on LinkedIn: https://www.linkedin.com/in/chiphuyen/Timestamps:[00:00] Chip's preferred coffee[00:39] Takeaways and introduction to Chip Huyen[05:33] Chip's Discord server[08:12] MLOps evangelist[10:27] Chip's system of learning[13:30] Having too many expectations makes you scared[14:10] How Chip went about her book and courses[18:49] When and when not to go real-time[21:03] Complexity with streaming or real-time[23:08] Company mistakes that should be avoided[26:53] Chip's focus[32:15] The topic Chip wished she had focused on[36:24] What part of streaming do people need to understand more about?[42:12] China is advanced in MLOps[47:06] Chip's interesting bucket list[50:18] You don't publish everything you write[53:16] Chip's Places to learn[56:13] Chip's journey in and out of academe[57:35] Closing

MLOps Coffee Sessions #132 {Podcast BTS} with Ian Schweer, What is Data / ML Like on League? co-hosted by Skylar Payne.  Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// AbstractIf 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.  // BioOn 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 jobs.mlops.community // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links Landing page:https://www.riotgames.com/en--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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 for the reality of writing production code[55:00] What would you change your career into?[57:00] Ian's best practices advice[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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// 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  jobs.mlops.community // 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/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠// Abstract Design ML to be components in a larger system with stable interfaces is not traceable 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  jobs.mlops.community// MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch 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/Timestamps:[00:00] Introduction to Andrew Yates and takeaways[03:26] Want more like this episode?[03:53] Andrew's Background[05:29] How did he get into adtech?[09:40] Evolution of adtech[12:30] Challenges they face[14:04] The structures of teams in bigger tech companies[21:12] Search and discovery teams in bigger tech companies[23:10] Strategy around technical debt[28:40] Promoted.ai for big marketplaces[30:18] How Andrew fits into teams[33:53] Engineering challenges when working in a small team[37:47] How much white-gloving they do amid complexity[39:32] Allowing companies to plug in their models into Promoted[41:58] Drawbacks of doing real-time streaming[48:06] Wrap up