Rekha Seshadrinathan

EchoStar evaluated 11 AI assistants before selecting Amazon Quick, which was then deployed to 8,000 employees with training and pre-populated skills. Next in EchoStar’s plans are integrating Quick with a third party gateways for observability and governance.

Amazon SageMaker HyperPod streamlines compute and workload scheduling and management, allowing you to have access to compute resources faster and optimize spend more intelligently.

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.

Amazon SageMaker offers different integrated development environments (IDEs) that are purpose-built for machine learning (ML). In this chalk talk, learn how to select and use your preferred environment to perform end-to-end ML development steps, from preparing data to building, training, and deploying your ML models.

Amazon SageMaker Studio is an integrated development environment (IDE) for data science and machine learning (ML). In this session, explore how to use SageMaker Studio to prepare data and build, train, deploy, and manage ML models from a single, web-based visual interface.