📋 Tender Overview
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, 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. Today’s announcements 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 is also introducing 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. To get started with Amazon SageMaker, visit aws.amazon.com/sagemaker.
Recent advancements in ML, along with ready availability of scalable compute capacity and the massive proliferation of data, have led to the rise of models that contain billions of parameters, making them capable of performing a wide range of tasks like writing blog posts, generating images, solving math problems, engaging in dialog, and answering questions based on a document. Today, tens of thousands of customers like 3M, AstraZeneca, Ferrari, LG AI Research, RyanAir, Thomson Reuters, and Vanguard are using SageMaker to make more than 1.5 trillion inference requests every month. In addition, customers like AI21 Labs, Stability AI, and Technology Innovation Institute are using SageMaker to train models with up to billions of parameters. As customers move from building mostly task-specific models to the large, general-purpose models that power generative AI, they work with massive datasets and more complex infrastructure setups—all while optimizing for cost and performance. Customers also want to build and customize their own models to create unique customer experiences, embodying the company’s voice, style, and services. With more than 380 capabilities and features added since the service was launched in 2017, SageMaker offers customers everything they need to build, train, and deploy production-ready models at scale.
“Machine learning is one of the most profound technological developments in recent history, and interest in models has spread to every organization,” said Bratin Saha, vice president of Artificial Intelligence and Machine Learning at AWS. “This growth in interest is presenting new scaling challenges for customers who want to build, train, and deploy models faster. From accelerating training, optimizing hosting costs, reducing latency, and simplifying the evaluation of foundation models, to expanding our no-code model-building capabilities, we are on a mission to democratize access to high-quality, cost-efficient machine learning models for organizations of all sizes. With today’s announcements, we are enhancing Amazon SageMaker with fully managed, purpose-built capabilities that help customers make the most of their machine learning investments.”
New capabilities make it easier and faster for customers to train and operate models to power their generative AI applications
As generative AI continues to gain momentum, many emerging applications will rely on models. But most organizations struggle to adapt their infrastructure to meet the demands of these new models, which can be difficult to train and operate efficiently at scale. Today, SageMaker is adding two new capabilities that help ease the burdens of training and deploying models at scale.
SageMaker HyperPod accelerates FM training at scale: Many organizations want to train their own models using graphics processing units (GPU)-based and Trainium-based compute instances at low cost. However, the volume of data, size of the models, and time required for training models has exponentially increased the complexity of training a model, requiring customers to further adapt their processes to deal with these emerging demands. Customers often need to split their model training across potentially hundreds or thousands of accelerators. They then run trillions of data computations in parallel for weeks or months at a time, which is time consuming and requires specialized ML expertise. The number of accelerators and training time increases substantially compared to training task-specific models, so the likelihood of rare, small errors, like a single accelerator failure, compounds. These errors can disrupt the entire training process and require manual intervention to identify, isolate, debug, repair, and recover from the issue, further delaying progress.
