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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

To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. 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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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

These models offer enterprises a range of capabilities, balancing accuracy, speed, and cost-efficiency. Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.

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Generative AI operating models in enterprise organizations with Amazon Bedrock

AWS Machine Learning

However, even in a decentralized model, often LOBs must align with central governance controls and obtain approvals from the CCoE team for production deployment, adhering to global enterprise standards for areas such as access policies, model risk management, data privacy, and compliance posture, which can introduce governance complexities.

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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. We will start by using the SageMaker Studio UI and then by using APIs.

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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

This blog post delves into how these innovative tools synergize to elevate the performance of your AI applications, ensuring they not only meet but exceed the exacting standards of enterprise-level deployments. More sophisticated metrics are needed to evaluate factual alignment and accuracy.

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