Category: Announcements

We’re pleased to announce the general availability (GA) of Amazon Bedrock Custom Model Import. This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API.

In this post, we discuss Bria’s family of models, explain the Amazon SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 model using SageMaker JumpStart.

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

Amazon Q Business recently added support for administrators to modify the default access control list (ACL) crawling feature for data source connectors. Amazon Q Business is a fully managed, AI powered assistant with enterprise-grade security and privacy features. It includes over 40 data source connectors that crawl and index documents. By default, Amazon Q Business […]

In this post we discuss how you can maintain access to Lookout for Vision after it is closed to new customers, some alternatives to Lookout for Vision, and how you can export your data from Lookout for Vision to migrate to an alternate solution.

In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.

This post discusses how customers can maintain access to Amazon Monitron after it is closed to new customers and what some alternatives are to Amazon Monitron.

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.