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.
21 авг 2024
