Where should you store your ML features to power real-time ML predictions, and why? In this talk, Tecton’s Co-Founder and CTO, Kevin Stumpf, will discuss the tradeoffs made and lessons learned while building the Feature Stores at Uber, Tecton and …
[Open Source] Hamilton, a micro framework for creating dataframes, and its application at Stitch Fix
At Stitch Fix, we have 130+ “Full Stack Data Scientists” who, in addition to doing data science work, are also expected to engineer and own data pipelines for their production models. One data science team, the Forecasting, Estimation, and Demand …
Twitter’s Feature Store Journey
A Feature Store is an essential piece of a production ML system. Twitter’s journey of building Feature Stores began several years ago. Since then, we have gone through multiple iterations of our Feature Store to facilitate creating, organizing, …
ML Projects Aren’t An Island
We’ve all seen the dismal and (at this point, annoying) charts and graphs of ‘>90.x% of ML projects fail’ used as marketing ploys by various companies. What this largely simplified view of ML project success rates buries in misleading abstraction …
Managing Data Infrastructure with Feast
Feast provides a simple framework for defining and serving machine learning features. In order to serve features reliably, with low latency and at high scale, Feast relies heavily on cloud infrastructure such as DynamoDB or AWS Lambda. This talk …
Using Feast in a Ranking System
This will be a practical session explaining how Better.com uses Feast in a Ranking System that depends on multiple data sources and several models. We’ll provide a walkthrough of several architectures we considered as a team to manage features, …
