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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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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. This request contains the user’s message and relevant metadata.

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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning - AI

These indexes enable efficient searching and retrieval of part data and vehicle information, providing quick and accurate results. The agents also automatically call APIs to perform actions and access knowledge bases to provide additional information.

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Streamlining Workflows with Feature Branches and Logical Stacks

Xebia

This blog explores how to optimize feature branch workflows, maintain encapsulated logical stacks, and apply best practices like resource naming to improve clarity, scalability, and cost-effectiveness. The CheckoutProcess name describes what it is, a role used by, for example, a lambda function that processes the checkout.

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

AWS Machine Learning - AI

These meetings often involve exchanging information and discussing actions that one or more parties must take after the session. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. Finally, video or audio files uploaded are stored securely in an S3 bucket.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

In synchronous orchestration, just like in traditional process automation, a supervisor agent orchestrates the multi-agent collaboration, maintaining a high-level view of the entire process while actively directing the flow of information and tasks. However, it can create bottlenecks as all operations must pass through the supervisor agent.

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Create generative AI agents that interact with your companies’ systems in a few clicks using Amazon Bedrock in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Manual processes and fragmented information sources can create bottlenecks and slow decision-making, limiting teams from focusing on higher-value work. The chat agent bridges complex information systems and user-friendly communication. This streamlined process enhances productivity and customer interactions across the organization.