Amazon SageMaker Unified Studio is in preview release and is subject to change.
Amazon Bedrock IDE in Amazon SageMaker Unified Studio
Amazon Bedrock IDE in Amazon SageMaker Unified Studio enables you to easily build and scale generative AI applications. Amazon Bedrock IDE provides a web interface that allow users to interact with Amazon Bedrock foundation models and use Amazon Bedrock tools, such as Agents, Guardrails, Prompts, Flows, Evaluation, and Functions in a seamless unified fashion. Users can interact with models in a generative AI playground or collaborate on developing generative AI applications in projects.
Amazon Bedrock IDE in Amazon SageMaker Unified Studio can be only used by the members of the Amazon SageMaker platform domains. For more information about accessing Amazon Bedrock IDE as a user via Amazon SageMaker Unified Studio, see Amazon Bedrock IDE in the Amazon Sagemaker Unified Studio User Guide.
In the current release of Amazon SageMaker Unified Studio, there are the following configuration paths available for setting up Amazon Bedrock IDE in an Amazon SageMaker platform domain, each offering a different level of customization:
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Quick setup - you can use this option as part of creating an Amazon SageMaker platform domain. Quick setup option simplifies the process of setting up Amazon Bedrock IDE by automating key steps without requiring user input. When selected during domain creation, the Quick setup performs the following:
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Creates the Generative AI application development project profile that the Amazon SageMaker Unified Studio user then uses to create Amazon SageMaker IDE projects.
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Activates all generative AI blueprints and the default Tooling blueprint needed to provision resources for the Amazon Bedrock capabilities.
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Configures permissions for all enabled Amazon Bedrock serverless models accessible in the AWS account and Region, enabling their use in the generative AI projects and playgrounds.
For more informaiton about creating an Amazon SageMaker platform domain with Quick setup, see Create a Amazon SageMaker Unified Studio domain - quick setup.
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Guided setup - this is the guided setup with a step-by-step walkthrough of configuring Generative AI capabilities for your Amazon SageMaker platform domains. You can use this option only after you've created your Amazon SageMaker platform domain by navigating to the domain details page and using the Next steps for your domain section. It pre-populates system-recommended configurations which you can review and modify. Key steps include:
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Creating the Generative AI application development project profile - the system generates a project profile specific to the domain's AWS account and Region. This step also automatically enables the generative AI blueprints if they are not already enabled. If the Tooling blueprint is not yet enabled in the domain, the system augments steps to enable it as well.
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Configuring model access - the system identifies all Amazon Bedrock serverless models available in the account and Region, then configures access permissions for these models. You can review the model list and selectively enable models for use in Amazon Bedrock IDE projects and domain playgrounds.
For detailed steps of using the guided setup of Generative AI capabilities for your Amazon SageMaker platform domain, see Configure Amazon Bedrock IDE for your Amazon SageMaker platform domain.
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Manual setup - this is a step-by-step configuration of project profiles, blueprints, and model access with granular control over configurations. You can use this option only after you've created your Amazon SageMaker platform domain by navigating to the domain details page and using the configuration settings under the Project profiles, Blueprints, and Amazon Bedrock models tabs. Manual setup is recommended for advanced scenarios, such as enabling generative AI in a different Region or account from the domain. Manual setup includes:
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Manually creating custom project profiles
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Enabling specific blueprints
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Once Amazon Bedrock IDE in Amazon SageMaker Unified Studio for a domain is set up, you can perform the following procedures to further customize and configure Amazon Bedrock IDE in for your domain users in Amazon SageMaker Unified Studio.
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Configure access to your Amazon Bedrock serverless models for the selected AWS accounts and regions
You can configure access to your Amazon Bedrock serverless models for Amazon Bedrock IDE projects and playgrounds by enabling or disabling access in the Amazon Bedrock models tab. To configure access, follow these steps:
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Navigate to the Amazon SageMaker management console at https://console.aws.amazon.com/datazone
and use the region selector in the top navigation bar to choose the appropriate AWS Region. -
Choose View domains and then choose the domain where you want to manage your Amazon Bedrock serverless models.
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Choose the Amazon Bedrock models tab.
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Amazon Bedrock IDE uses the Amazon Bedrock to enable model interaction. The IDE allows you to use the model that you have granted access in Amazon Bedrock. An Amazon Bedrock IDE project can only access models from the project’s AWS account and AWS Region. A playground can access models from any account and region. On the Amazon Bedrock models page, under the Select account and region section, choose the AWS account and Region where you want to manage your serverless models.
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On the Amazon Bedrock models page, under the Models enabled for the selected account and region section, choose the refresh icon.
The system queries Amazon Bedrock and displays a list of Amazon Bedrock serverless models to which you have access. If no models are listed or if a specific model is missing, visit the Amazon Bedrock management console for the appropriate account and Region to grant access. If you have updated model access in Amazon Bedrock, choose the refresh icon in the Amazon Bedrock Models tab to refresh the updated list of accessible models
The following are important elements to consider as you review the generated list of models:
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Every model in the list is prepopulated with certain details, including modality, inference type, whether it's enabled in projects and playground, and roles for model access. A model's modality indicates the type of output data it can generate. Amazon Bedrock IDE in Amazon SageMaker Unified Studio supports Amazon Bedrock foundation models with on-demand throughput and on-demand cross-region inference. If a model supports both on-demand and on-demand cross-region inference, it appears in the list twice with the appropraite value listed in the Inference column. You have the flexibility to enable your preferred inference type for use in projects and playgrounds. Amazon Bedrock IDE in the Amazon SageMaker Unified Studio does NOT support provisioned throughput, custom models, or imported models.
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For easy setup, the system pre-selects accessible models that support on-demand throughput, excluding legacy models, to enable in projects and playground. Review and adjust the list to enable models for projects and playgrounds based on your specific requirements.
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The models that you have not yet enabled for projects and playground access are grayed out in the list. To enable or disable access, choose Manage and then use the checkboxes in the Enable in project and Enable in playground columns. If you choose to disable project access of a model, confirm the disable action in the pop up window that appears.
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To enable or disable your models in the Amazon SageMaker Unified Studio projects and playgrounds, choose Manage and then use the checkboxes in the Enable in project and Enable in playground columns to enable or disable your models. If you choose to disable a model in a project, confirm the disable action in the pop up window that appears.
Set default models for the generative AI playgrounds in Amazon SageMaker Unified Studio
Amazon Bedrock IDE in the Amazon SageMaker Unified Studio supports generative AI playgrounds that enable the Amazon SageMaker platform domain users to easily experiment with Amazon Bedrock models. Users can send prompt requests to various models and view the responses. There are two types of playgrounds in the Amazon Bedrock IDE in the Amazon SageMaker Unified Studio: the chat playground and the image and video playground.
As the administrator of an Amazon SageMaker platform domain, you can complete the following procedure to set default chat and video and image generative AI playgrounds in the Amazon SageMaker Unified Studio for your domain and their respective default models. When users access the playground, the default model is preselected for them to begin interacting.
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Navigate to the Amazon SageMaker management console at https://console.aws.amazon.com/datazone
and use the region selector in the top navigation bar to choose the appropriate AWS Region. -
Choose View domains and then choose the domain where you want to configure the detaul playgrounds and models.
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Choose the Amazon Bedrock models tab.
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In the Default models section, choose Manage.
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On the Default models - optional page:
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For the Chat playground - optional, select a default model from the drop-down menu. The drop-down menu includes only the models that support Text as the output modality and are enabled for playground use.
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For the Image and video playground - optional, select a default model from the drop-down menu. The drop-down menu will include only the models that support either Image or Video as the output modality and are enabled for playground use.
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Choose Save to save your choices for the default playgrounds and their respective default models.
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Publishing models from associated accounts
Amazon Bedrock models are published to the domain as model assets through the GenerativeAIModelGovernanceProject project. This project is is created by Amazon SageMaker Unified Studio automatically and by default, the IAM identity (IAM user or role) who configures Amazon Bedrock IDE for the domain is the owner of this project. If the domain was created via Quick setup, SSO users that were configured during Quick setup are also owners of the GenerativeAIModelGovernanceProject project.
If you want to publish models from your associated account, the IAM identity of the associated account must be added to the GenerativeAIModelGovernanceProject project. In the current release of Amazon SageMaker Unified Studio, you must complete the following procedure to do this:
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Find the ID of the GenerativeAIModelGovernanceProject from the list projects output by running this command:
aws datazone list-projects --domain-identifier=<domain-id> --region <region> -
Add the IAM identity from the associated account to the domain and note the user ID in the response:
aws datazone create-user-profile --domain-identifier=<domain-id> --region <region> --user-type=IAM_USER --user-identifier=<iam-identity-arn>For example:
<iam-identity-arn> can be 'arn:aws:iam::<account-id>:role/Admin' -
Add the user to the GenerativeAIModelGovernanceProject:
aws datazone create-project-membership --domain-identifier=<domain-id> --region <region> --project-identifier=<project-id> --member='{"userIdentifier": <"user-id">}' --designation=PROJECT_OWNER
