Rama Thamman

Join this chalk talk to explore how SageMaker HyperPod serves as a unified GPU pool for data processing, training, post-training, and inference — no re-platforming required.

Amazon SageMaker offers the highest-performing ML infrastructure and a resilient training environment to help you train foundation models (FMs) for months without disruption. Top AI companies, from enterprises to startups, build cutting-edge models with billions of parameters on SageMaker.

As you advance in your journey from evaluating foundation models (FMs) to building generative AI applications at scale, you need services to deploy these models at the best price performance.

High-performance, cost-effective model deployment is critical to maximize the return on your ML investments. Amazon SageMaker provides the breadth and depth of fully managed deployment features to achieve optimal inference performance and cost, while reducing operational burden.

The TensorFlow deep learning framework is widely used in academia and industry. Using Amazon SageMaker, organizations can quickly begin a fully managed TensorFlow experience.