A Point in Time: Mutable Data in Online Inference

Posted Mar 28, 2021 | Views 955

# Data engineering

# Systems and Architecture

SPEAKER

Orr Shilon

Machine Learning Engineering Team Lead @ Lemonade

Orr is a ML Engineering Team Lead at Lemonade, currently developing a unified ML Platform. His teamÕs work aims to increase development velocity, improve accuracy, and promote visibility into machine learning at Lemonade.

Previously, Orr worked at Twiggle on semantic search, at Varonis, and at Intel. He holds a B.Sc. in Computer Science and Psychology from Tel Aviv University.

Orr also enjoys trail running and sometimes races competitively.

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Previously, Orr worked at Twiggle on semantic search, at Varonis, and at Intel. He holds a B.Sc. in Computer Science and Psychology from Tel Aviv University.

Orr also enjoys trail running and sometimes races competitively.

+ Read More

SUMMARY

Most business applications mutate relational data. Online inference is often done on this mutable data, so training data should reflect the state at the prediction's "point in time" for each object. There are a number of data architecture / domain modeling patterns which solve this issue, but they only work from implementation date onwards.

In this talk we'll suggest how to use the "point in time" as a first-class citizen in your ML Platform, while still striving to maximize the use of your older messier data.

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