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


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.

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