"Real-Time" ML: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #135

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