We walk you through how we adopted Feast at Adyen. We’ll discuss the decisions we made because of infra and tech constraints, and the customizations we added— in particular for our open source project, spark-offline-store, which was adopted into …
[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 …
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, …
Using Redis as your Online Feature Store: 2021 highlights & 2022 directions
With the growing business demand for real-time predictions, we are witnessing companies making investments in modernizing their data architectures to support online inference. When companies need to deliver real-time ML applications to support …
High Performance Feature Serving with Feast on AWS
In this lightning talk we will showcase how teams can deploy and productionize a feature store on AWS with Feast. We will demonstrate how users can train a machine learning model using historical features from Redshift, deploy the model into …
How Robinhood Built a Feature Store Using Feast
Features are essential to ML models. Therefore, a good feature infrastructure is important to any organization that wants to use ML properly in production. Feast is a great tool for building up your feature infrastructure. However, using Feast in …
How Shopify Contributed to Scale Feast
This talk will discuss how Shopify manages large volumes of ML data (billions of rows) using Feast. Shopify decided to adopt Feast to build their ML Feature Store in early 2021. We will speak about how we contributed to Feast to make it more scalable …
Programmatic Supervision for Software 2.0
One of major bottlenecks in the development and deployment of AI applications is the need for the massive labeled training datasets that drive modern ML approaches today. These training datasets traditionally are often labeled by hand at great time …
Bringing Feast 0.10 to AWS
In this lightning talk, we will cover our vision, the current state, and the roadmap for bringing Feast 0.10 to AWS. We will take a deep dive into the core composable API, and into Amazon DynamoDB and Amazon S3 connectors for online and offline …
Redis as an Online Feature Store
Feature stores are becoming an important component in any ML/AI architecture today. What is a feature store? – In a nutshell, the feature store allows you to build and manage the features for your training phase (offline feature store) and inference …
