Description
One of the key questions in modern data science and machine learning, for businesses and practitioners alike, is how do you move machine learning projects from prototype to production as a repeatable process. In this talk, we present an introduction to the landscape of production-grade tools, techniques, and workflows that bridge the gap between laptop data science and production ML workflows. In doing so, we’ll present a widely applicable stack that provides the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for foundational infrastructure.