MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference co-hosted by Skylar Payne.
// Abstract
Moving 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 do this move, what tools to use, and what steps to take.
// Bio
Sasha 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 co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many more things.
Rupesh Gupta
Rupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.
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Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupesh
Timestamps:
[00:00] Sasha's and Rupesh's preferred coffee
[01:30] Takeaways
[07:23] Changes in LinkedIn
[09:21] "Real-time" Machine Learning in LibnkedIn
[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
