Tecton
timezone
+00:00 GMT
SIGN IN
  • Home
  • Events
  • Content
  • Help
Sign In
Sign in or Join the community to continue

A Point in Time: Mutable Data in Online Inference

Posted Mar 28
# Data engineering
# Systems and Architecture
Share
SPEAKER
Orr Shilon
Orr Shilon
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.

+ Read More

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.

+ 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.

+ Read More

Watch More

10
Posted Mar 28 | Views 129
# Data engineering
# Feature Stores
# Open Source
10
Posted Apr 12 | Views 18
# Model serving
# Model training
# Production Use Case
# Systems and Architecture
30
Posted Apr 12 | Views 22
# Data engineering
# Explainability and Observability
# Feature Stores
# Research
# Systems and Architecture