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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG).

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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

Its AI/ML solutions drive enhanced operational efficiency, productivity, and customer experience for many of their enterprise clients. Wipro has used the input filter and join functionality of SageMaker batch transformation API. The response is returned to Lambda and sent back to the application through API Gateway.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

With the advancements in automation and configuring with increasing levels of abstraction to set up different environments with IaC tools, the AWS CDK is being widely adopted across various enterprises. The following diagram illustrates the solution architecture.

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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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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

The majority of enterprise customers already have a well-established MLOps practice with a standardized environment in place—for example, a standardized repository, infrastructure, and security guardrails—and want to extend their MLOps process to no-code and low-code AutoML tools as well. For this post, you use a CloudFormation template.