At DoorDash, machine learning is a key component, used to enhance the experience of merchants, dashers, and customers. As our machine learning use cases keep growing, our forecasting and training pipelines are faced with several challenges like scalability, growing costs, reduced user development velocity and lack of proper debugging/observability.
Driven by these challenges, we started learning about Ray and implemented a POC to verify the feasibility of incorporating Ray into our existing ML Platform architecture. In this talk, we'll share our journey towards adopting Ray, working with the open source KubeRay, how we built our POC and benchmarking setup and also share what the future of ML Platform at DD looks like with Ray being a core component.
Swaroop is a Staff Engineer in the Machine Learning Platform team at DoorDash, focused on areas of forecasting, model training, model observability and feature engineering. He was previously an engineering manager leading a team of data scientists and machine learning engineers at Helpshift, building ML products in the domain of customer service. He is also the author of the online beginner's book "A Byte of Python".
Dhaval works at DoorDash on their ML Platform team with his primary focus being model training. Previously he worked at Electronic Arts, where he initially worked on text-to-speech ML use cases and later on their ML Platform
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