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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2

AWS Machine Learning

In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. The agent can use company APIs and external knowledge through Retrieval Augmented Generation (RAG). If you already have an OpenAPI schema for your application, the best practice is to start with it.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. This data provides a benchmark for expected agent behavior, including the interaction with existing APIs, knowledge bases, and guardrails connected with the agent.

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Revolutionize logo design creation with Amazon Bedrock: Embracing generative art, dynamic logos, and AI collaboration

AWS Machine Learning

Amazon Bedrock enables access to powerful generative AI models like Stable Diffusion through a user-friendly API. In the media and entertainment sector, filmmakers, artists, and content creators can use this as a tool for developing creative assets and ideating with images. This API will be used to invoke the Lambda function.

APIs 125
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Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. In this post, we illustrate how to handle OOC by utilizing the power of the IMDb dataset (the premier source of global entertainment metadata) and knowledge graphs.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning

Growing in the media and entertainment space, Veritone solves media management, broadcast content, and ad tracking issues. Amazon Transcribe The transcription for the entire video is generated using the StartTranscriptionJob API. The metadata generated for each video by the APIs is processed and stored with timestamps.

APIs 122
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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. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

APIs 116