Data analytics and AI-ML model development project profile - Amazon SageMaker Unified Studio
Configure data analytics and AI/ML model development capability for your Amazon SageMaker platform domainCreate a Data analytics and AI-ML model development project profile

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Data analytics and AI-ML model development project profile

The Data analytics and AI-ML model development project profile enables your Amazon SageMaker Unified Studio users to consume, process, and produce data assets with AWS Glue, Amazon EMR, Amazon SageMaker, MWAA, and Amazon Redshift Serverless.

You can use the following procedures to create a data analytics and AI-ML model development project profile.

Configure data analytics and AI/ML model development capability for your Amazon SageMaker platform domain

Complete the following procedure to configure data analytics and AI/ML model development capability 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 data analytics and AI/ML model development.

  3. On the domain's details page, under the Next steps for your domain section, choose the Configure button next to the Data analytics and AI/ML model development domain capability.

  4. On the Create project profile: Data analytics and AI-ML model development page, in the Data analytics and AI-ML model development section, review the capabilities, tools, and functionalities that are enabled for this project profile.

  5. On the Create project profile: Data analytics and AI-ML model development, expand the Default tooling blueprint deployment settings section and review the settings, including the Tooling blueprint deployment account and region.

    Important

    Note that by configuring the data analytics and AI/ML model development 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 Data analytics and AI-ML model development project profile or Custom project profile.

  6. On the Create project profile: Data analytics and AI-ML model development, in the Enable blueprints section, review the following blueprints that will be enabled for this project profile.

    Important

    Note that by configuring the data analytics and AI/ML model development capability for your domain (this procedure), you can only enable these blueprints in the same AWS account and region as your domain. To enable these blueprints in an account or region that's different from that of your domain's, see Create a Data analytics and AI-ML model development project profile or Custom project profile.

    • MLExperiments

    • Workflows

    • LakehouseCatalog

    • EmrOnEc2

    • Tooling

    • RedshiftServerless

    • DataLake

    • EmrServerless

  7. On the Create project profile: Data analytics and AI-ML model development page, in the Manage access role section, specify a service role that gives Amazon SageMaker Unified Studio authorization to ingest and manage access to datashares, tables and views in Amazon Redshift. You can create a new or using an existing role.

  8. On the Create project profil: Data analytics and AI-ML model development page, in the Provisioning role section, specify a service role that gives Amazon SageMaker Unified Studio authorization to ingest and manage access to datashares, tables and views in Amazon Redshift.

  9. On the Create project profile: Data analytics and AI-ML model development page, in the Amazon S3 bucket for blueprints section, specify an Amazon S3 bucket for blueprints in your AWS account.

  10. On the Create project profile: Data analytics and AI-ML model development page, in the Networking section, specify a VPC in which to provision your Amazon SageMaker 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.

  11. On the Create project profile: Data analytics and AI-ML model development 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 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.

  12. Choose Create project profile.

After you complete this procedure, your Data analytics and AI-ML model development project profile for this domain is created and all the supported blueprints for it are enabled. Your domain users can then proceed to use this project profile to create prjects in Amazon SageMaker Unified Studio.

Create a Data analytics and AI-ML model development project profile

Complete the following procedure to create a Data analytics and AI-ML model development project profile for your Amazon SageMaker platform domain. Once this procedure is complete, your Data analytics and AI-ML model development project profile will only include the capabilities defined in the Tooling blueprint. To configure the full data analytics and AI-ML model development capability for your Amazon SageMaker platform domain, you must then use the Blueprints tab and configure the following blueprints for this project profile:

  • MLExperiments

  • Workflows

  • LakehouseCatalog

  • EmrOnEc2

  • RedshiftServerless

  • DataLake

  • EmrServerless

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 Data analytics and AI-ML model 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 Data analytics and AI-ML model 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.

Important

After you complete this procedure, your Data analytics and AI-ML model development project profile will only include the capabilities defined in the Tooling blueprint. You can further customize this project profile and configure it to include the full supported Data analytics and AI-ML model development capability by using the Bluerpints tab to enable the rest of its required bluerpints. They are the following:

  • MLExperiments

  • Workflows

  • LakehouseCatalog

  • EmrOnEc2

  • RedshiftServerless

  • DataLake

  • EmrServerless