AWS Machine Learning Blog

Category: Launch

Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

Amazon Bedrock has launched a capability that organizations can use to tag on-demand models and monitor associated costs. Organizations can now label all Amazon Bedrock models with AWS cost allocation tags, aligning usage to specific organizational taxonomies such as cost centers, business units, and applications.

Snowflake Arctic models are now available in Amazon SageMaker JumpStart

Today, we are excited to announce that the Snowflake Arctic Instruct model is available through Amazon SageMaker JumpStart to deploy and run inference. In this post, we walk through how to discover and deploy the Snowflake Arctic Instruct model using SageMaker JumpStart, and provide example use cases with specific prompts.

Cohere Rerank 3 Nimble now generally available on Amazon SageMaker JumpStart

The Cohere Rerank 3 Nimble foundation model (FM) is now generally available in Amazon SageMaker JumpStart. This model is the newest FM in Cohere’s Rerank model series, built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems. In this post, we discuss the benefits and capabilities of this new model with some examples. Overview […]

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions of entities, which are inherently embedded in most real-world […]

Amazon SageMaker now integrates with Amazon DataZone to streamline machine learning governance

Unlock ML governance with SageMaker-DataZone integration: streamline infrastructure, collaborate, and govern data/ML assets.

Experience the new and improved Amazon SageMaker Studio

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE) […]

Launch processing jobs with a few clicks using Amazon SageMaker Data Wrangler

August 2023: This post was reviewed for accuracy. Amazon SageMaker Data Wrangler makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. Previously, when you created a Data Wrangler data flow, you could choose different export options to easily integrate that data flow into […]