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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. We walk you through our solution, detailing the core logic of the Lambda functions. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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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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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

When API Gateway receives the request, it triggers an AWS Lambda The Lambda function sends the question to the classifier LLM to determine whether it is a history or math question. These embeddings are then saved as a reference index inside an in-memory FAISS vector store, which is deployed as a Lambda layer.

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

An email handler AWS Lambda function is invoked by WorkMail upon the receipt of an email, and acts as the intermediary that receives requests and passes it to the appropriate agent. The system indexes documents and files stored in Amazon Simple Storage Service (Amazon S3) using Amazon OpenSearch Service for quick retrieval.

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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning - AI

The solution also uses Amazon Cognito user pools and identity pools for managing authentication and authorization of users, Amazon API Gateway REST APIs, AWS Lambda functions, and an Amazon Simple Storage Service (Amazon S3) bucket. To launch the solution in a different Region, change the aws_region parameter accordingly.

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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning - AI

At its core, Amazon Simple Storage Service (Amazon S3) serves as the secure storage for input files, manifest files, annotation outputs, and the web UI components. Pre-annotation and post-annotation AWS Lambda functions are optional components that can enhance the workflow. On the SageMaker console, choose Create labeling job.

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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. Alternatively, you can use AWS Lambda and implement your own logic, or use open source tools such as fmeval. For example, in one common scenario with Cognito that accesses resources with API Gateway and Lambda with a user pool.