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: Alexander Ng is a Senior Data Engineer at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to Manifold, Alex served as both a Sales Engineering Tech Lead and a DevOps Tech Lead for Kyruus, a startup that built SaaS products for enterprise healthcare organizations. Alex got his start as a Software Systems Engineer at the MITRE Corporation and the Naval Undersea Warfare Center in Newport, RI. His recent projects at the intersection of systems and machine learning continue to combine a deep understanding of the entire development lifecycle with cutting-edge tools and techniques. Alex earned his Bachelor of Science degree in Electrical Engineering from Boston University, and is an AWS Certified Solutions Architect.