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Engineering Systems for Real-Time Predictions @DoorDash
Recorded at:

| by Raghav Ramesh Follow 0 Followers on Aug 22, 2018 | NOTICE: The next QCon is in San Francisco Nov 5 - 9, 2018. Save an extra $100 with INFOQSF18!
33:20

Summary
Raghav Ramesh presents DoorDash’s thoughts on how to structure machine learning systems in production to enable robust and wide-scale deployment of machine learning, and shares best practices in designing engineering tooling around machine learning.

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Bio

Raghav Ramesh is a machine learning engineer at DoorDash working on its core logistics engine, where he focuses on AI problems: vehicle routing, Dasher assignments, delivery time predictions, demand forecasting, and pricing. Previously, he worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics.

Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams.

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