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Real-time Machine Learning: Features and Inference

Posted Nov 29, 2022 | Views 719
# Real-time Machine Learning
# ML Inference
# ML Features
# LinkedIn
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SPEAKERS
Sasha Ovsankin
Sasha Ovsankin
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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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
Rupesh Gupta
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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Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Skylar Payne
Skylar Payne
Skylar Payne
Machine Learning Engineer @ HealthRhythms

Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!

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Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!

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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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