Amazon Web Services (AWS) has announced five new capabilities within Amazon SageMaker to help accelerate building, training, and deployment of large language models and other foundation models.
As models continue to transform customer experiences across industries, SageMaker is making it easier and faster for organizations to build, train, and deploy machine learning (ML) models that power a variety of generative AI uses cases.
However, to use models successfully, customers need advanced capabilities that efficiently manage model development, usage, and performance. That’s why most industry leading models such as Falcon 40B and 180B, IDEFICS, Jurassic-2, Stable Diffusion, and StarCoder are all trained on SageMaker.
The announcements by (AWS) include a new capability that further enhances SageMaker for scaling with models by accelerating model training time. Another new SageMaker capability optimizes managed ML infrastructure operations by reducing deployment costs and latency of models.
AWS has also introduced a new SageMaker Clarify capability that makes it easier to select the right model based on quality parameters that support responsible use of AI. To help customers apply these models across organizations, AWS is also introducing a new no-code capability in SageMaker Canvas that makes it faster and easier for customers to prepare data using natural-language instructions.
Additionally, SageMaker Canvas continues to democratize model building and customization by making it easier for customers to use models to extract insights, make predictions, and generate content using an organization’s proprietary data. These advancements build on SageMaker’s extensive capabilities to help customers innovate with ML at scale.
