Feature stores have emerged as a pivotal component in the modern machine learning stack, as more data scientists and engineers work together to operationalize ML. Associated with this task are some operational challenges. The toughest challenges for operationalizing ML is data: how to compute and select features, store, validate serve, discover and share them.

In this talk you will learn:

• what key problems feature stores solve to operationalize ML
• why features stores are a pivotal components in the model machine learning stack
• common key use cases and deployment patterns for feature stores observed by the MLOps and ML practitioners
• how feature stores are playing a transformational role with the rise of modern data platforms
• get a glimpse into a popular open source feature store Feast