Generative AI application development project profile - Amazon SageMaker Unified Studio
Configure Amazon Bedrock IDE for your Amazon SageMaker platform domainCreate a generative AI application development project profile

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Generative AI application development project profile

A Generative AI application development project profile enables generative AI solutions from Amazon Bedrock for your Amazon SageMaker platform domains. It provides project users in Amazon SageMaker Unified Studio with the access to the following generative AI tools: Bedrock Chat Agents, Bedrock Knowledge Bases, Bedrock Guardrails, Bedrock Functions, Bedrock Flows, Bedrock Prompts, and Bedrock Evaluations.

You can complete the following procedures to create a Generative API application development project profile.

Configure Amazon Bedrock IDE for your Amazon SageMaker platform domain

Complete the following procedure to configure Amazon Bedrock IDE for your Amazon SageMaker platform domain.

  1. 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.

  2. Either create a new domain or choose an existing domain where you want to configure Amazon Bedrock IDE.

  3. On the domain's details page, under the Next steps for your domain section, choose the Configure button next to the Generative AI domain capability.

  4. On the Create project profile: Amazon Bedrock generative AI page, locate the Generative AI blueprints section and review the settings.

    As part of configuring Amazon Bedrock IDE for your Amazon SageMaker platform domain (this procedure) you are creating the Generative AI application development project profile and therefore you must enable the blueprints that contain the tools, resources, and parameters that this project profile requires. The following blueprints are enabled when you create this project profile as part of this procedure:

    • AmazonBedrockChatAgent

    • AmazonBedrockKnowledgeBase

    • AmazonBedrockGuardrail

    • AmazonBedrockFunction

    • AmazonBedrockFlow

    • AmazonBedrockPrompt

    • AmazonBedrockEvaluation

    Important

    Note that by configuring Amazon Bedrock IDE for your domain (this procedure), you can only enable the generative AI blueprints for this project profile in this domain's AWS account and Region. To enable these blueprints in an account or region that's different from that of your domain's (associated account), see Create a generative AI application development project profile or Custom project profile.

    Under Provisioning role, specify a new or existing service role that is to be used by Amazon SageMaker Unified Studio to provision and manage resources defined in the selected blueprints in your account.

  5. On the Create project profile: Amazon Bedrock generative AI page, locate the Default tooling blueprint deployment settings section that contains the Tooling bluepring deployment settings used to create projects from this project profile and review them and modify the following as needed. Note that if you have already enabled the Tooling blueprint, you cannot use this procedure to modify any of the Tooling blueprint settings.

    • Under Manage access role, specify a service role that gives Amazon SageMaker Unified Studio the authorization to create and configure project resources using AWS CloudFormation in the project account and region. If this service role already exists in this AWS account, it is selected by default.

    • For the Tooling blueprint deployment account and region, note that by configuring Amazon Bedrock IDE capability for your domain (this procedure), you can only enable the Tooling blueprint in the same AWS account and region as your domain. To enable the Tooling blueprint in an account or region that's different from that of your domain's, see Create a generative AI application development project profile and Custom project profile.

    • In the Amazon S3 bucket for blueprints section, specify an Amazon S3 bucket for blueprints in your AWS account.

    • In the Networking section, in the Virtual private cloud (VPC) setting, choose a VPC in which to provision your Amazon SageManker platform domain. VPCs tagged with Amazon SageMaker Unified Studio should be correctly configured.

      In the Subnets section, select at least 3 subnets in different Availability Zones that contain required VPC Endpoints. Private subnets are recommended, not all functionality is available when selecting public subnets.

    • In the Data encryption section, your data is encrypted by default with a key that AWS owns and manages for you. Encryption cannot be changed after the domain is created. Choose either Use AWS owned key (a key that AWS owns and manages for you) or the Choose a different AWS KMS key (advanced) (a key that you have permissions to use, or create a new one) and then specify an existing or create a new AWS KMS key.

  6. On the Create project profile: Amazon Bedrock generative AI page, in the Authorization - optional section, specify who can use this project profile to create projects in all domain units. This can also be done per domain unit in the Amazon SageMaker Unified Studio. Choose either Selected users and groups (select which users and groups are authorized to use this project profile) or Allow all users and groups (allow any user to use this project profile).

    Note

    Projects do not provide strong security isolation. To limit cross-domain and cross-project resource discovery you can consider creating projects in separate accounts.

  7. On the Create project profile: Amazon Bedrock generative AI page, in the Permissions for Bedrock model access section, specify the permissions for users to interact with the enabled Amazon Bedrock models. The system can automatically create roles to control user access and interactions with these models or you can specify existing roles.

    For the Model provisioning role, you can create a new or use an existing role. The system uses the role you specify as the provisioning role to create an inference profile that has access to an Amazon Bedrock model in a project. The role you specify here is used as the provisioning role for all the Amazon Bedrock models enabled for this domain.

    For the Model consumption role, you can create a new or use an existing role. The system uses a consumption role to grant users access to Amazon Bedrock models in the playground in the Amazon SageMaker Unified Studio.

  8. Choose Next to advance to the Configure model access page.

  9. On the Configure model access page, in the Models section, you can configure access to your Amazon Bedrock serverless models by enabling or disabling them for this domain.

    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:

    • 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. Amazon Bedrock IDE in the Amazon SageMaker Unified Studio does NOT support provisioned throughput, custom models, or imported models.

    • 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.

    • If the model that you want to manage for your Amazon SageMaker Unified Studio users is not present in the list, make sure that it has been enabled for access in Amazon SageMaker Unified Studio. This is done in the Amazon Bedrock management console. For more information, see Amazon Bedrock Documentation.

  10. On the Configure model access page, in the Default models - optional section, you can 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.

    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.

    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.

  11. Choose Finish to complete configuring Amazon Bedrock IDE for this domain.

Once the action is successfully completed and you've finished configuring Amazon Bedrock IDE for this domain, you are redirected to the domain's details page where you can find the enabled generative AI blueprints under the Blueprints tab, a Generative AI project profile under the Project profiles tab, and the enabled models listed in the Amazon Bedrock models tab. Note, that you can manage model access directly from Amazon Bedrock models tab. For more information, see Amazon Bedrock IDE in Amazon SageMaker Unified Studio

Create a generative AI application development project profile

Complete the following procedure to create a Generative AI application development project profile for your Amazon SageMaker platform domain. Once this procedure is complete, your Generative AI application development project profile will only include the capabilities defined in the Tooling blueprint. To configure the full generative AI application development capability for your Amazon SageMaker platform domain, you must then use the Blueprints tab and configure the AmazonBedrockGenerativeAI blueprint for this project profile. The AmazonBedrockGenerativeAI blueprint contains the following generative AI blueprints:

  • AmazonBedrockChatAgent

  • AmazonBedrockKnowledgeBase

  • AmazonBedrockGuardrail

  • AmazonBedrockFunction

  • AmazonBedrockFlow

  • AmazonBedrockPrompt

  • AmazonBedrockEvaluation

Important

Note that when you enable a blueprint, by default, you are enabling it in the same region as your domain. When you are enabling blueprints for a project profile that is created and enabled in a different region from your domain, you must enable these blueprints in same region where this project profile is enabled (in addition to enabling this blueprint in the same region as your domain). You can do this via the Regions tab in the blueprint details page. This applies to all blueprints, including the Tooling blueprint.

  1. 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.

  2. Either create a new domain or choose an existing domain where you want to create a generative AI application development project profile.

  3. On the domain's details page, choose the Project profiles tab and then choose Create.

  4. On the Create project profile page, in the Project profile name and description section, specify the name of the project profile and the description.

  5. On the Create project profile page, in the Project profile creation options section, choose Create from a template, and then under Project profile templates, choose Generative AI application development.

  6. On the Create project profile page, in the Default tooling blueprint deployment settings section, review the selections for the default deployment settings for the Tooling blueprint.

    Important

    Note that by creating this project profile from a template, you can either enable the Tooling blueprint in the same AWS account and region as your domain (prepopulated by default) or you can enable the Tooling blueprint in a different AWS account and region from this domain (an associated account).

  7. On the Create project profile page, in the Authorization - optional section, specify who can use this project profile to create projects in all domain units. This can also be done per domain unit in the Amazon SageMaker Unified Studio. You can specify Selected users and groups or Allow all users and groups options.

    Note

    Projects do not provide strong security isolation. To limit cross-domain and cross-project resource discovery you can consider creating projects in separate accounts.

  8. On the Create project profile page, in the Project profile readiness section, specify whether you want to enable this project profile on creation. Unless you check the Enable project profile on creation checkbox, your project profile is disabled and not available to use for Amazon SageMaker Unified Studio projects after its creation. Leaving a project profile in a disabled state upon creation gives you the opportunity to customize your blueprints before making the project profile available.

  9. Choose Create project profile.