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 at enterprise scale? It is imperative to plan for deployment from day one, both in tool selection and in the feedback and development process.
As recently as a few years ago, data scientists were the people who played in a sandbox—when they came up with a useful model, it was thrown over the wall to another team that would reimplement it to put it into production. Those days are over now: there’s only one Git repo in the entire company, and everything you commit is essentially in production. But teams are still run as if data science is mainly about experimentation.
This tutorial presents best practices for working in this new reality. Data scientists can still play in a sandbox, but do it in a way such that it’s turnkey to take models into production. Just as DevOps is about people working at the intersection of development and operations, there are now people working at the intersection of data science and software engineering who need to be integrated into the team with tools and support. At Manifold, we’ve developed the Lean AI process and the open-source Orbyter package for Docker-first data science to help do just that.
Sourav Day and Alex Ng explain how to streamline a machine learning project and help your engineers work as an an integrated part of your development and production teams.
Sourav Dey is CTO at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to 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 in Electrical Engineering and Computer Science from the Massachusetts Insitute of Technology (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 degree from Boston University in Electrical Engineering.
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