Giuseppe Angelo Porcelli

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 HyperPod helps build, train and deploy AI models at scale. You can spin-up preferred IDE on HyperPod to develop their models and run experiments, then scale to thousands of accelerators to run distributed training and finally deploy models for real-time or batch inference.

Foundation models are able to solve a wide array of tasks, however to solve most tasks effectively, these models require some form of customization. In this session, you’ll learn about how you can get started with advanced customization of foundation models using SageMaker AI, while optimizing cost and performance.

Amazon SageMaker Studio offers comprehensive tools for end-to-end ML development—from preparing data to training models, tracking experiments, deploying models, and managing pipelines—all in an integrated development environment.

AI/ML can make you a disruptive innovator in any industry. As an active sponsor and contributor to Project Jupyter, our goal at AWS is to help Jupyter become the best possible notebook platform for data science and machine learning.

In this chalk talk, discover how you can optimize ML models in production with an Amazon SageMaker real-time endpoint. Walk through ways to load test the endpoint for scale, throughput, latency, and cost.

Amazon SageMaker is a fully managed service that removes the heavy lifting from each step of the machine learning (ML) workflow and provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. In this interactive workshop, we work on the different aspects of the ML workflow to build, train, and deploy a model using all the capabilities of Amazon...