Open Source Summit + Embedded Linux Conference + OSPOCon 2021

Having access to a consistent set of dataset features during different phases of the ML lifecycle is becoming critical. Companies that build and deploy machine learning models may need to manage hundreds of features, and they may even require using the latest features for real time prediction. Feast (Feature Store) attempts to tackle these problems by providing a standard interface and store for retrieving features needed for distributed model training and serving. In this talk, attendees will learn how to build a production ready feature store on Kubernetes by using Feast which will be used to serve the data to the model. Additionally, attendees will see how Feast can be used with KFServing, a serverless model inferencing engine, to retrieve stored features in real time. In this talk, we hope to share how users can get started with using Feast on Kubernetes for their ML needs. Here, we set up an end-to-end demo using Feast and  KFserving transformer on Kubernetes to demonstrate how online features can be served to the KFserving for real time inferencing.