Prerequisite: None
Enterprises must build greater governance controls around “features,” which are the predictive variables that machine learning (ML) models use to predict some quantifiable outcome of interest. To the extent that data scientists can reuse previously discovered features in future ML projects, they can streamline and accelerate the process under which statistical models are built, trained, and deployed into production.
In this presentation, TDWI’s James Kobielus will discuss the evolution of data analytics in the cloud, with a specific focus on the increasing importance of “feature stores,” which are specialized governance repositories for ensuring consistency and compliance within and across repeatable MLOps pipelines.