The feature store community has been laser focused on building the necessary infrastructure for putting ML in production. But what happens when you have that infrastructure in place? In this talk, we will move up a level to show you how to build ML systems with a feature store. We will show how to leverage the feature, training and inference pipelines paradigm to develop and productionize ML systems both in batch and real time.

We will show how to apply solid software engineering and MLOps principles for change control, staged feature rollout and availability to deliver high quality features in real time for model predictions.

Register here.