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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization.

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Amazon Bedrock launches Session Management APIs for generative AI applications (Preview)

AWS Machine Learning

Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Building generative AI applications requires more than model API calls.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic.

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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning

As companies of all sizes continue to build generative AI applications, the need for robust governance and control mechanisms becomes crucial. topicPolicyConfig={ 'topicsConfig': [ { 'name': 'In-Person Tutoring', 'definition': 'Requests for face-to-face, physical tutoring sessions.', 'examples': [ 'Can you tutor me in person?'

APIs 118
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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. When using the RetrieveAndGenerate API, the output includes the generated response, the source attribution, and the retrieved text chunks.

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Amazon Bedrock Guardrails announces IAM Policy-based enforcement to deliver safe AI interactions

AWS Machine Learning

Beyond Amazon Bedrock models, the service offers the flexible ApplyGuardrails API that enables you to assess text using your pre-configured guardrails without invoking FMs, allowing you to implement safety controls across generative AI applicationswhether running on Amazon Bedrock or on other systemsat both input and output levels.

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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning

Use natural language in your Amazon Q web experience chat to perform read and write actions in ServiceNow such as querying and creating incidents and KB articles in a secure and governed fashion. ServiceNow Obtain a ServiceNow Personal Developer Instance or use a clean ServiceNow developer environment.

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