// 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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Timestamps:

[00:00] Sasha's and Rupesh's preferred coffee

[01:30] Takeaways

[07:37] Changes in LinkedIn

[09:34] "Real-time" Machine Learning

[13:22] Value of Feedback

[14:37] Technical details behind getting the most recent information integrated into the models

[17:06] Embedding Vector Search action occurrence

[18:46] Meaning of "Real-time" Features and Inference

[20:36] Are "Real-time" Features always worth that effort and always helpful?

[26:08] Challenges in "Real-time" Features

[31:36] System design review on Pinterest

[39:29] Learnings to share

[46:34] Branching for Machine Learning

[49:41] Not so talked about discussion of "Real-time"