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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents. For this post, we recommend activating these models in the us-east-1 or us-west-2 AWS Region.

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Moderate your Amazon IVS live stream using Amazon Rekognition

AWS Machine Learning Blog

You can deploy this solution to your AWS account using the AWS Cloud Development Kit (AWS CDK) package available in our GitHub repo. Using the AWS Management Console , you can create a recording configuration and link it to an Amazon IVS channel. In this section, we briefly introduce the system architecture.

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Build a water consumption forecasting solution for a water utility agency using Amazon Forecast

AWS Machine Learning Blog

Create a new AWS Identity and Access Management (IAM) role. For What-if forecast definition method , select Use transformation functions. Conclusion In this post, we showed you how easy to use how to use Forecast and its underlying system architecture to predict water demand using water consumption data.

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Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

AWS Machine Learning Blog

KubeRay creates the following custom resource definitions (CRDs): RayCluster The primary resource for managing Ray instances on Kubernetes. Alternatively and recommended, you can deploy a ready-made EKS cluster with a single AWS CloudFormation template. in the aws-do-ray GitHub repo. The fsdp-ray.py

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning Blog

The absence of centralized workflow definitions means that message processing occurs naturally based on publication timing and agent availability, creating a fluid and adaptable system that can evolve with changing requirements. For more information about when to use AWS Config, see AWS AppConfig use cases.

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Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

AWS Machine Learning Blog

AWS FSI customers, including NASDAQ, State Bank of India, and Bridgewater, have used FMs to reimagine their business operations and deliver improved outcomes. It requires precise, formal definition of rules and isnt suitable for subjective decisions that require human judgment. However, its important to understand its limitations.

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