Hugo Bowne-Anderson - Full-stack Machine Learning and Generative AI for Data Scientists
www.pydata.org
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 and experiment to production as a repeatable process. In this tutorial, 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. We’ll cover a wide range of applications, including business-critical ML and data pipelines of today, as well as state-of-the-art generative AI and LLM use cases of tomorrow.
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