Disciplined ML Engineering: MLOps Best Practices from the Trenches

Abstract: Artificial Intelligence is already helping many businesses become more responsive and competitive, but how do you move machine learning models efficiently from research to deployment? It is imperative to plan for deployment from day one, both in tool selection and in the feedback and development process. Additionally, just as DevOps is about people working at the intersection of development and operations, ML engineers are now working at the intersection of data science and software engineering, and need to be integrated into the team with tools and support.

At Manifold, we've developed the Lean AI process to streamline machine learning projects and the open-source Orbyter package for Docker-first data science to help your ML engineers work as an an integrated part of your development and production teams. In this workshop, Sourav and Alex demonstrate how to use Orbyter to spin up containers for local ML development work and discuss experiment management as part of the Lean AI process—sharing best practices we learned in on-the-ground work with a range of clients.

Bio: As CTO for Manifold, Sourav is responsible for the overall delivery of data science and data product services to make clients successful. Before Manifold, Sourav led teams to build data products across the technology stack, from smart thermostats and security cams (Google / Nest) to power grid forecasting (AutoGrid) to wireless communication chips (Qualcomm). He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He earned his PhD, MS, and BS degrees from MIT in Electrical Engineering and Computer Science.