Remove Examples Remove Lambda Remove Machine Learning
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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

With this solution, you can interact directly with the chat assistant powered by AWS from your Google Chat environment, as shown in the following example. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For example, a marketing content creation application might need to perform task types such as text generation, text summarization, sentiment analysis, and information extraction as part of producing high-quality, personalized content. An example is a virtual assistant for enterprise business operations.

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

AWS Machine Learning - AI

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. You can invoke Lambda functions from over 200 AWS services and software-as-a-service (SaaS) applications.

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Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

invoke(input_text=Convert 11am from NYC time to London time) We showcase an example of building an agent to understand your Amazon Web Service (AWS) spend by connecting to AWS Cost Explorer , Amazon CloudWatch , and Perplexity AI through MCP. In the first flow, a Lambda-based action is taken, and in the second, the agent uses an MCP server.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

The following diagram illustrates an example architecture for ingesting data through an endpoint interfacing with a large corpus. Step Functions orchestrates AWS services like AWS Lambda and organization APIs like DataStore to ingest, process, and store data securely. The fetched data is put into an S3 data store bucket for processing.

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

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

Through code examples and step-by-step guidance, we demonstrate how you can seamlessly integrate this solution into your Amazon Bedrock application, unlocking a new level of visibility, control, and continual improvement for your generative AI applications.