Category: Announcements

In this post, we show how you can discover and deploy the Llama 3.2 11B Vision model using SageMaker JumpStart. We also share the supported instance types and context for all the Llama 3.2 models available in SageMaker JumpStart.

This is the first time that the Llama models from Meta have been released with vision capabilities. These new capabilities expand the usability of Llama models from their traditional text-only applications. In this post, we demonstrate how you can use Llama 3.2 11B and 90B models for a variety of vision-based use cases.

In this post, we present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge.

In this post, we show you how the new document-level report in Amazon Kendra provides enhanced visibility and observability into the document processing lifecycle during data source sync jobs.

AWS DeepComposer was first introduced during AWS re:Invent 2019 as a fun way for developers to compose music by using generative AI. AWS DeepComposer was the world’s first machine learning (ML)-enabled keyboard for developers to get hands-on—literally—with a musical keyboard and the latest ML techniques to compose their own music. After careful consideration, we have […]

In this post, we discuss the core capabilities of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances and the use cases they’re well-suited for. We walk you through an example of how to get started with these instances and carry out inference deployment of Meta Llama 3.1 70B and 405B models on them.

In this post, we demonstrate how to fine-tune the recently released Llama 3 models from Meta, specifically the llama-3-8b and llama-3-70b variants, using Amazon SageMaker JumpStart.

In this post, we explore the new features of Model Registry that streamline foundation model (FM) management: you can now register unzipped model artifacts and pass an End User License Agreement (EULA) acceptance flag without needing users to intervene.

Today, we are happy to announce the general availability of cross-region inference, a powerful feature allowing automatic cross-region inference routing for requests coming to Amazon Bedrock. This offers developers using on-demand inference mode, a seamless solution for managing optimal availability, performance, and resiliency while managing incoming traffic spikes of applications powered by Amazon Bedrock. By opting in, developers no longer have to spend time and effort predicting demand fluctuations.

Today, we are excited to announce general availability of batch inference for Amazon Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.