Remove Auto-classification Remove Auto-complete Remove Generative AI
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Building Generative AI prompt chaining workflows with human in the loop

AWS Machine Learning Blog

Generative AI is a type of artificial intelligence (AI) that can be used to create new content, including conversations, stories, images, videos, and music. Like all AI, generative AI works by using machine learning models—very large models that are pretrained on vast amounts of data called foundation models (FMs).

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. When this is complete, the document can be routed to the appropriate department or downstream process. Custom classification is a two-step process.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. With a data flow, you can prepare data using generative AI, over 300 built-in transforms, or custom Spark commands. For Problem type , select Classification.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

These generative AI applications are not only used to automate existing business processes, but also have the ability to transform the experience for customers using these applications.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

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Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod

AWS Machine Learning Blog

Thomson Reuters , a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. Legal research is a critical area for Thomson Reuters customers—it needs to be as complete as possible. 55 440 0.1 164 64 512 0.1

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

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Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

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