Feature stores have emerged as critical technologies in a modern ML stack. They aim to solve the full set of data management problems encountered when building and operationalizing data for ML applications.
In this webinar we will dive into the design and concepts of feature stores, where feature stores fit in the ML stack, and the problems they solve. We will then provide a hands on walk through in deploying an end-to-end ML system that leverages a Feast as its feature store, and Kubeflow as the ML platform.
Willem Pienaar (Tecton)
Willem is currently a tech lead at Tecton where he leads the development of Feast, an open source feature store for machine learning. Previously he led the ML platform team at Gojek, the Southeast Asian decacorn, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision making. In a previous life, Willem founded and sold a networking startup.