Speaker: Raghav Ramesh

Engineering systems for real-time predictions @DoorDash

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, Raghav worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics. He holds an MS from Stanford University, where he focused on artificial intelligence and operations research.

Talk : Engineering Systems for Real-Time Predictions @DoorDash

Track : Practical Machine Learning
Location : Empire Complex, 7th fl.
Day of week : Wednesday
Duration : 1:40pm - 2:30pm

Today, applying machine learning to drive business value in a company requires a lot more than figuring out the right algorithm to use; it requires tools and systems to manage the entire machine learning product lifecycle. For instance, we need systems to manage data pipelines, to monitor model performance and detect degradations, to analyze data quality and ensure consistency between training and prediction environments, to experiment with different versions of models, and to periodically retrain models and automatically deploy them.

At DoorDash, an on-demand logistics company, we fulfill deliveries on a dynamic marketplace, which requires extensive use of real-time predictions. Through many iterations of applying machine learning in our products, we identified solutions to address the above problems and  built these into our machine learning platform. This has dramatically reduced the cost of integrating machine learning into our products, saved us weeks of development time, and allowed us to use ML in new product areas.

In this talk, we will present our thoughts on how to structure machine learning systems in production to enable robust and wide-scale deployment of machine learning and share best practices in designing engineering tooling around machine learning.

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