Feature Stores: an Operational Bridge Between ML Models and Data | PyData Global 2021

Feature Stores: an Operational Bridge Between Machine Learning Models and Data Speakers: Jules S. Damji, Danny Chiao Summary 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. Description 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 pivota