Sasha discusses the concept of real-time machine learning and its applications in streaming systems and real-time systems like robotics and self-driving cars. He questions the usefulness of adding real-time features to recommender systems and suggests optimizing the model structure and batch-computed features should be the focus before considering adding time features.
MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, "Real-time" Machine Learning: Features and Inference co-hosted by Skylar Payne.
Link to the full episode: [ Ссылка ]
// 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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Near real-time features for near real-time personalization blog:
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