Pooja Karadgi

With dozens of models now available and new ones launching every week, customers face three questions: which models to start from, when does model customization unlock added performance or savings, and when to replace a model, either because their own needs changed or because a better model emerged.

Learn how to customize foundation models for your specific business needs. In this chalk talk, we'll discuss various customization approaches including supervised fine-tuning, reinforcement learning, and explore when to apply each technique most effectively.

Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs), reducing training time by up to 40%.