Reproducible Data Science Using Orbyter

Abstract: 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. Additionally, 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 to streamline machine learning projects and the open-source Orbyter package for Docker-first data science to help your engineers work as an an integrated part of your development and production teams. In this workshop, Sourav and Alex will focus heavily on the DevOps side of things, demonstrating how to use Orbyter to spin up data science containers and discussing experiment management as part of the Lean AI process.

Bio: 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.