Tools & Techniques for Bringing Machine Learning Research to Production

Full Title:
Tools & Techniques for Bringing Machine Learning Research to Production

Abstract:
As a data scientist on a machine learning research team, I often find myself treading the line between the wild west of research and the unrelenting demands of production-quality software development. Because of the nature of machine learning research, traditional software development methodologies don't necessarily apply, and new approaches are needed. These issues are compounded by the fact that the worlds of machine learning, DevOps and cloud computing are always changing and it seems impossible to keep up-to-date. In this talk, I will discuss these challenges and present some lessons learned and best practices. In particular I will demonstrate how to leverage Python's packaging system as a valuable tool to tackle these issues and conclude with an example research-to-production CI/CD pipeline.

Short Bio:
Devin Conathan has had a long and winding road to his current position of Senior Data Scientist on the Machine Learning Research team at American Family Insurance. He has undergraduate degrees in mathematics and philosophy from Cornell University and a masters in electrical engineering from the University of Wisconsin-Madison. Between his undergraduate and graduate studies, he spent much time figuring out what he wanted to do with his life in various roles such as specialty coffee barista and cafe manager, customer support and sales representative hawking music gear, amateur crêpier and wannabe cheesemonger. In his current role he enjoys developing full-stack solutions that leverage state-of-the-art machine learning research. His recent work mainly involves natural language processing, information retrieval, knowledge graphs and knowledge graph embeddings. He lives in Madison, Wisconsin with his lovely wife and fluffy cat, and he spends most of his free time learning various musical instruments and rewatching (again) episodes of The Office.

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