Efficient ML engineering: Tools and best practices
Who is this presentation for?Data engineers, data architects, developers
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, MLOps. 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 Alexander Ng highlight the six steps of the Lean AI process and explain how it helps your ML engineers work as 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.
- A basic understanding of the software engineering process
- Familiarity with machine learning vocabulary (model, training, etc.)
Materials or downloads needed in advance
- A laptop
What you'll learn
- Discover how to get value from machine learning in a way that affects your company's bottom line by building teams of data scientists and engineers that are well integrated into organizational teams delivering models into production
Sourav Dey is CTO at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Previously, Sourav led teams building data products across the technology stack, from smart thermostats and security cams at Google Nest to power grid forecasting at AutoGrid to wireless communication chips at Qualcomm. He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He holds PhD, MS, and BS degrees in electrical engineering and computer science from MIT.
Alexander Ng is a senior data engineer at Manifold. His previous work includes a stint as engineer and technical lead doing DevOps at Kryuus as well as engineering work for the Navy. He holds a BS in electrical engineering from Boston University.
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