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Medical content creation in the age of generative AI

AWS Machine Learning - AI

The solution has been designed using the following services: Amazon Elastic Container Service (ECS) : to deploy and manage our Streamlit UI. Amazon Lambda : to run the backend code, which encompasses the generative logic. In step 5, the lambda function triggers the Amazon Textract to parse and extract data from pdf documents.

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Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications

AWS Machine Learning - AI

Now that you understand the concepts for semantic and hierarchical chunking, in case you want to have more flexibility, you can use a Lambda function for adding custom processing logic to chunks such as metadata processing or defining your custom logic for chunking. Make sure to create the Lambda layer for the specific open source framework.

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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

Solution overview This section outlines the architecture designed for an email support system using generative AI. High Level System Design The solution consists of the following components: Email service – This component manages incoming and outgoing customer emails, serving as the primary interface for email communications.

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Create a generative AI-based application builder assistant using Amazon Bedrock Agents

AWS Machine Learning - AI

The agent can recommend software and architecture design best practices using the AWS Well-Architected Framework for the overall system design. Create and associate an action group with an API schema and a Lambda function. Recommend AWS best practices for system design with the AWS Well-Architected Framework guidelines.

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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning - AI

Consider the following system design and optimization techniques: Architectural considerations : Multi-stage prompting – Use initial prompts for data retrieval, followed by specific prompts for summary generation. For example, “Cross-reference generated figures with golden source business data.” Don’t make up any statistics.”

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Import a question answering fine-tuned model into Amazon Bedrock as a custom model

AWS Machine Learning - AI

To set up SageMaker Studio, refer to Launch Amazon SageMaker Studio. Refer to the SageMaker JupyterLab documentation to set up and launch a JupyterLab notebook. For more details, refer to Evaluate Bedrock Imported Models. He specializes in generative AI, artificial intelligence, machine learning, and system design.

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21 Best Practices for Migrating to React JS

Modus Create

“Build one to throw away” shouldn’t refer to your flagship product. Allow yourself time to vet and review references. Good examples are AWS Lambda or Cloudflare Workers. Use a Design System. Good leadership will create a sense of ownership. Invest in your team; train the team early in the process.