Parth Patel & Isha Dua - Optimizing GenAI Workloads on AWS

As GenAI models grow more complex, successfully optimizing their accuracy, cost efficiency, and deployment velocity on AWS becomes both increasingly critical and challenging. This session will provide an end-to-end guide for GenAI teams to maximize their workload efficiency on AWS. We’ll provide an overview of the full GenAI development lifecycle then do a deep dive into progressive model optimization techniques-from prompt engineering to RAG and fine tuning-exploring how each incrementally improves accuracy while minimizing retraining compute requirements. Shifting gears, we’ll share AWS best practices for optimizing GenAI model packaging, deployment, and inference using containers and hardware acceleration. Monitoring and maintaining production GenAI workloads presents its own challenges, which we’ll address through observability, data drift detection, and model degradation monitoring techniques and tools on AWS. Attendees will walk away with a clear framework for incrementally optimizing their GenAI workloads throughout the machine learning lifecycle with an eye toward maximizing performance while keeping costs in check.
LinkedIn - / patel-parth-g
LinkedIn - / ishneet-dua-isha-1a515068
Slides - docs.google.co...
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