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Anthropic's Claude 3.7 Sonnet is now available on Amazon Bedrock in AWS GovCloud (US-West)

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Anthropic’s Claude 3.7 Sonnet hybrid reasoning model is now available in AWS GovCloud (US-West). Claude 3.7 Sonnet offers advanced AI capabilities with both quick responses and extended, step-by-step thinking made visible to the user. This model has strong capabilities in coding and brings enhanced performance across various tasks, like instruction following, math, and physics. Anthropic’s Claude 3.7 Sonnet model is also FedRAMP High and Department of Defense Cloud Computing Security Requirements Guide (DoD CC SRG) Impact Level (IL) 4 and 5 approved within Amazon Bedrock in the AWS GovCloud (US) Regions.

Claude 3.7 Sonnet introduces a unique approach to AI reasoning by integrating it seamlessly with other capabilities. Unlike traditional models that separate quick responses from those requiring deeper thought, Claude 3.7 Sonnet allows users to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. In extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks. Amazon Bedrock customers can adjust how long the model thinks, offering a flexible trade-off between speed and answer quality. Additionally, users can control the reasoning budget by specifying a token limit, enabling more precise cost management.

Claude 3.7 Sonnet is also available on Amazon Bedrock in the Europe (London), Europe (Frankfurt), Europe (Ireland), Europe (Paris), Europe (Stockholm), US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, visit the Amazon Bedrock console . Integrate it into your applications using the Amazon Bedrock API or SDK. For more information, see the AWS News Blog , Claude in Amazon Bedrock and the Amazon Bedrock documentation .

Fully managed MLflow 3.0 now available on Amazon SageMaker AI

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Amazon SageMaker now offers fully-managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your GenAI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.

As customers across industries accelerate their generative AI development, they require capabilities to track experiments, observe behavior, and evaluate performance of models and AI applications. Data scientists and developers lack tools for analyzing the performance of models and AI applications from experimentation to production, making it hard to root cause and resolve issues. Teams spend more time integrating tools than improving their models or generative AI applications. With this launch, fully managed MLflow 3.0 on Amazon SageMaker AI enables customers to accelerate generative AI by making it easier to track experiments and monitor performance of models and AI applications using a single tool. Tracing capabilities in fully managed MLflow 3.0 enable customers to record the inputs, outputs, and metadata at every step of a generative AI application, helping developers quickly identify the source of bugs or unexpected behaviors. By maintaining records of each model and application version, fully managed MLflow 3.0 offers traceability to connect AI responses to their source components, allowing developers to quickly trace an issue directly to the specific code, data, or parameters that generated it. This dramatically reduces troubleshooting time and enables teams to focus more on innovation.

Fully managed MLflow 3.0 on Amazon SageMaker AI is now available in all regions where Amazon SageMaker is offered, excluding China Regions and GovCloud (US) Regions.

To learn more about fully managed MLflow 3.0 on Amazon SageMaker AI, visit the Amazon SageMaker developer guide .

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