At Instacart, we are building our next generation ML Training Platform on Ray. We build standard training runtime for broad ML use cases across Instacart, and at the same time provide capabilities for more advanced users to fine tune their distributed deep learning model built by Ray AIR.
We'll demonstrate the end to end system built on top of KubeRay for workflow orchestration, as well as the standard runtime deeply integrated with Ray Data, Ray Train and Ray Tune to utilize distributed computation capabilities across all training lifecycles.
We'll also demonstrate a more custom use case, Instacart personalization ranker, which is also built on Ray AIR but with more complex data preprocessing steps. We'll tell the story of how we fine tune p13n ranker model to achieve higher training throughput and other optimizations brought by streaming executor etc.
Han is a senior machine learning engineer at Instacart, leading the projects of Instacart next generation Training Platform. Before Instacart, Han worked on distributed training platform at Meta AI Infra. Han pursed her master degree on Computer Engineering at University of Texas at Austin.
Rajpal is an Engineering Leader on the ML Foundations team and leads the team that develops Model Training, Serving, Monitoring, and Adaptive Experimentation Infrastructure at Instacart. He has over 17 years of experience and has worked as a Data Platform Lead, Architect, Backend developer, DevOps, and QA engineer in that time. He has Masters's degree in Computer Science from San Diego and a bachelor's degree in Electronics Engineering from Mumbai, India.
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