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

AWS Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

AWS Machine Learning

However, there are benefits to building an FM-based classifier using an API service such as Amazon Bedrock, such as the speed to develop the system, the ability to switch between models, rapid experimentation for prompt engineering iterations, and the extensibility into other related classification tasks.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes.

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Integrate generative AI capabilities into Microsoft Office using Amazon Bedrock

AWS Machine Learning

Note that these APIs use objects as namespaces, alleviating the need for explicit imports. API Gateway supports multiple mechanisms for controlling and managing access to an API. AWS Lambda handles the REST API integration, processing the requests and invoking the appropriate AWS services.

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

AWS Machine Learning

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. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

By documenting the specific model versions, fine-tuning parameters, and prompt engineering techniques employed, teams can better understand the factors contributing to their AI systems performance. SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process.