Efficient ML engineering: Tools and best practices

Description

Business value comes from solving real needs by putting models into production. You need to be able to move ML models efficiently from research to deployment at enterprise scale. Part of the answer is about using the right workflow, and the other part is about choosing the right tools. The recent rise of the ML engineer is in large part due to evolving workflow best practices: just as DevOps folks have been working at the intersection of development and operations, today, ML engineers are working at the intersection of data science and software engineering—that is, ML ops. These folks must be integrated into the team with efficient tools and effective support. Manifold developed the Lean AI process and the open source Orbyter package for Docker-first data science to streamline the development process and help companies put successful models into production as smoothly and efficiently as possible. Even if you’ve never used Docker before, Orbyter makes containerization simple and elegant—which in turn makes your team’s work seamless and clean.

Sourav Dey and Jakov Kucan walk you through the six steps of the Lean AI process and explain how it helps your ML engineers work as an an integrated part of your development and production teams. You’ll get a hands-on example using real-world data, so you can get up and running with Docker and Orbyter and see firsthand how streamlined they can make your workflow. They cover creating an AI specification by understanding both your business and your data; using containerized data science for cleaner workflows (no experience needed); developing ML engineering as a core competency; being deliberate, disciplined, and coordinated with your process; and deploying seamlessly at production scale.