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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning

Using the job ID and message ID returned by the previous request, the client connects to the WebSocket API and sends the job ID and message ID to the WebSocket connection. A Lambda function invokes the Amazon Textract API DetectDocument to parse tabular data from source documents and stores extracted data into DynamoDB.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference. The Step Functions state machine, S3 bucket, Amazon API Gateway resources, and Lambda function codes are stored in the GitHub repo. The following figure illustrates our Step Function workflow.

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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

AWS Machine Learning

We present our solution through a fictional consulting company, OneCompany Consulting, using automatically generated personalized website content for accelerating business client onboarding for their consultancy service. In this solution, the LLM is asked to use the sentence without changes because it’s a testimonial.