MLOps: ML Engineering Best Practices from the Trenches
MLOps: ML Engineering Best Practices from the Trenches


DevOps tools for getting code reliably to production have proven to be effective in the software engineering world. Today, ML Engineers are working at the intersection of data science and software engineering, and can leverage DevOps best practices to streamline their workflow and delivery process. This is what MLOps is all about.

At Manifold, we've developed processes to help ML engineers work as an an integrated part of your development and production teams, helping you to be deliberate, disciplined, and coordinated in your deployment process. In this workshop, Sourav and Alex will walk you through the some key learnings from using this Lean AI process. They’ll cover topics such as:
Scaffolding a project with a consistent structure for ML
Using Docker for a consistent runtime across the team and environments
Cleanly separating ML configuration from ML source code
Setting up an ML experiment tracking system
Systems for rapid ML experimentation in the cloud
Deploying ML seamlessly at production scale using Docker


Alexander Ng is a Director, Infrastructure & DevOps 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.

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