Solving MLOps: A First-Principles Approach to Machine Learning Production

Abstract
We love talking about deploying our machine learning models. One famous (but probably wrong) statement says that “87% of data science projects never make it to production.” But how can we get to the promised land of "Production" if we're not even sure what "Production" even means? If we could define it, we could more easily build a framework to choose the tools and methods to support our journey. Learn a first-principles approach to thinking about deploying models to production and MLOps. I'll present a mental framework to guide you through the process of solving the MLOps challenges and selecting the tools associated with machine learning deployments.

About the Speaker
Dean has a background combining physics and computer science. He’s worked on quantum optics and communication, computer vision, software development, and design. He’s currently CEO at DagsHub, where he builds products that enable data scientists to work together and get their models to production, using popular open-source tools. He’s also the host of the MLOps Podcast, where he speaks with industry experts about machine learning in production.