Using machine learning and generative AI in Amazon SageMaker Unified Studio
Note
Powered by Amazon Bedrock: AWS implements automated abuse detection. Because the AI recommendations for descriptions functionality in Amazon SageMaker Unified Studio is built on Amazon Bedrock, users inherit the controls implemented in Amazon Bedrock to enforce safety, security, and the responsible use of AI.
In the current release of Amazon SageMaker Unified Studio, you can use the AI recommendations for names & descriptions functionality to automate data discovery and cataloging. Support for generative AI in Amazon SageMaker Unified Studio creates business names and descriptions for assets and columns. You can use these names and descriptions to add business context for your data and recommend analysis for datasets, which can help boost data discovery results.
Powered by Amazon Bedrock's large language models, the AI recommendations for data asset names & descriptions in Amazon SageMaker Unified Studio help you to ensure that your data is comprehensible and easily discoverable. The AI recommendations also suggest the most pertinent analytical applications for datasets. By reducing manual documentation tasks and advising on appropriate data usage, auto-generated names and descriptions can help you to enhance the trustworthiness of your data and minimize overlooking valuable data to accelerate informed decision making.
Supported Regions
In the current Amazon SageMaker Unified Studio release, the AI recommendations for names and descriptions feature is supported in the following regions:
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US East (N. Virginia)
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US West (Oregon)
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Asia Pacific (Tokyo)
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Europe (Frankfurt)
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Asia Pacific (Sydney)
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Canada (Central)
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Europe (London)
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South America (Sao Paulo)
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Europe (Ireland)
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Asia Pacific (Singapore)
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US East (Ohio)
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Asia Pacific (Seoul)
Amazon SageMaker Unified Studio supports Business Description Generation in the following regions.
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Asia Pacific (Mumbai)
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Europe (Paris)
Amazon SageMaker Unified Studio supports Business Name Generation in the following regions.
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Europe (Stockholm)
Bedrock Cross Region Inference
Amazon SageMaker Unified Studio leverages Amazon Bedrock's Cross Region inference endpoint to serve recommendations for the US East (Ohio) region. All other regions use in-region endpoint.
Steps to use GenAI
The following procedure describes how to generate AI recommendations for names and descriptions in Amazon SageMaker Unified Studio:
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Navigate to Amazon SageMaker Unified Studio using the URL from your admin and log in using your SSO or AWS credentials.
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Choose the project that contains the asset for which you want to generate AI recommendations for descriptions.
Generating Business Descriptions and Summaries
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Navigate to the Data tab for the project.
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From Project catalog, choose Assets and chose the asset for which you want to generate AI recommendations for descriptions.
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On the asset's details page, in the Business metadata tab, choose Generate descriptions.
Generating Business Names
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Navigate to the Data tab for the project.
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In the left navigation pane, choose Data sources, and then choose datasource for which you want to enable business name generation.
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Go to the details tab and enable the AUTOMATED BUSINESS NAME GENERATION configuration.
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BusinessNames can also be generated programmatically when creating an asset by enabling the businessNameGeneration flag under predictionConfiguration in the CreateAsset API payload.
Accepting/Rejecting Predictions
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Once the descriptions are generated, you can either edit, accept, or reject them.
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Green icons are displayed next to each automatically generated metadata description for the data asset. In the Business metadata tab, you can choose the green icon next to the automatically generated Summary, and then choose Edit, Accept, or Reject to address the generated description.
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You can also choose Accept all or Reject all options that are displayed at the top of the page when the Business metadata tab is selected, and thus perform the selected action on all automatically generated descriptions.
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Or you can choose the Schema tab, and then address automatically generated descriptions individually by choosing the green icon for one column description at a time and then choosing Accept or Reject.
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In the Schema tab, you can also choose Accept all or Reject all and thus perform the selected action on all automatically generated descriptions.
To publish the asset to the catalog with the generated descriptions, choose Publish asset, and then confirm this action by choosing Publish asset again in the Publish asset pop up window.
Note
If you don't accept or reject the generated descriptions for an asset, and then you publish this asset, this unreviewed automatically generated metadata is not included in the published data asset.
Support for custom relational asset types
Amazon SageMaker Unified Studio supports genAI capabilities for custom asset types. Previously this feature was only supported for the managed AWS Glue and Amazon Redshift asset types.
In order to enable this feature, create your own asset type definition and attach
RelationalTableFormType as one of the forms. Amazon SageMaker Unified Studio
automatically detects the presence of such forms and enables GenAI capabilities for
these assets. The overall experience remains the same for generating business names
(via predictionConfiguration in the CreateAsset API) and businessDescription (via
Generate Description button click on the asset details page).
For more information about creating custom asset types see Create custom asset types in Amazon SageMaker Unified Studio.
Quotas
Amazon SageMaker Unified Studio supports different quotas for business name generation and business description generation. You can reach out to the AWS support team for an increase in these quotas.
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BusinessDescriptionGeneration: 10K invocations/month
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BusinessNameGeneration: 50K invocations/month
