Real-time Machine Learning: Features and Inference

Posted Nov 29, 2022 | Views 380

# Real-time Machine Learning

# ML Inference

# ML Features

SPEAKERS

Sasha Ovsankin

Software Engineer @ LinkedIn

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.

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Rupesh Gupta

Senior Staff Engineer @ LinkedIn

Rupesh is a Senior Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.

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Rupesh is a Senior Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.

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SUMMARY

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.

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