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19 hours ago

AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026) Last week, what excited me most was the launch of the 2026 AWS AI & ML Scholars program by Swami Sivasubramanian, VP of AWS Agentic AI, to provide free AI education to up to 100,000 learners worldwide. The program has two phases: a Challenge phase where you’ll learn foundational generative AI skills, followed by a […]

AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026)

Last week, what excited me most was the launch of the 202...

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1 week ago

Amazon SageMaker Unified Studio adds custom metadata filters Amazon SageMaker Unified Studio adds custom metadata search filters, enabling customers to narrow catalog search results using organization-specific attributes. This helps customers find the right assets faster by filtering on fields like business region, data classification, or study name, in addition to existing keyword and semantic search. With custom metadata search filters, customers can add filters based on any custom metadata fields available in their catalog, such as sample type or study ID. Filters support string fields with a "contains" operator and numeric fields (Integer, Long) with equals, greater than, and less than operators. Customers can also filter by asset name, description, and date range. Multiple filters can be combined, and filter selections persist across browser sessions. Custom metadata search filters are available in all AWS Regions where Amazon SageMaker Unified Studio is supported. Standard Amazon SageMaker pricing applies. To get started, navigate to the Browse Assets page in Amazon SageMaker Unified Studio and use the "+ Add Filter" button to create custom filters. You can also use the SearchListings API with metadata form attributes in the filters parameter. For more information, see the Amazon SageMaker Unified Studio documentation.

🆕 Amazon SageMaker Unified Studio now supports custom metadata filters for quicker asset searches via region, data classification, or study name, alongside keyword and semantic search. Available in all AWS Regions, standard pricing applies.

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1 week ago

Amazon SageMaker Unified Studio adds custom metadata filters Amazon SageMaker Unified Studio adds custom metadata search filters, enabling customers to narrow catalog search results using organization-specific attributes. This helps customers find the right assets faster by filtering on fields like business region, data classification, or study name, in addition to existing keyword and semantic search. With custom metadata search filters, customers can add filters based on any custom metadata fields available in their catalog, such as sample type or study ID. Filters support string fields with a "contains" operator and numeric fields (Integer, Long) with equals, greater than, and less than operators. Customers can also filter by asset name, description, and date range. Multiple filters can be combined, and filter selections persist across browser sessions. Custom metadata search filters are available in all AWS Regions where Amazon SageMaker Unified Studio is supported. Standard Amazon SageMaker pricing applies. To get started, navigate to the Browse Assets page in Amazon SageMaker Unified Studio and use the "+ Add Filter" button to create custom filters. You can also use the SearchListings API with metadata form attributes in the filters parameter. For more information, see the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/search-for-data.html.

Amazon SageMaker Unified Studio adds custom metadata filters

Amazon SageMaker Unified Studio adds custom metadata search filters, enabling customers to narrow catalog search results using organization-specific attributes. This helps customers find the right assets ...

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1 week ago

Amazon SageMaker Unified Studio supports aggregated view of data lineage https://aws.amazon.com/sagemaker/unified-studio/ now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete picture of data transformations and dependencies across your entire lineage graph, helping you quickly identify all upstream sources and downstream consumers of your datasets. Previously, SageMaker Unified Studio showed the lineage graph as it existed at a specific point in time, which is useful for troubleshooting and investigating specific data processing events. The aggregated view now provides a complete picture of data transformations and dependencies across multiple levels of the lineage graph. You can use this view to understand the full scope of jobs impacting your datasets and to identify all upstream sources and downstream consumers. The aggregated view is available as the default lineage view in Amazon SageMaker Unified Studio for IdC-based domains. You can switch to the previous view by toggling the "display in event timestamp order" option. You can also query the lineage graph using the new QueryGraph API, which provides lineage node graphs with metadata and augmented business context. Aggregated view of lineage is available in all existing Amazon SageMaker Unified Studio https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. For detailed information on how to get started with lineage using these new features, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/aggregated-lineage-view.html and https://docs.aws.amazon.com/boto3/latest/reference/services/datazone/paginator/QueryGraph.html.

Amazon SageMaker Unified Studio supports aggregated view of data lineage

https://aws.amazon.com/sagemaker/unified-studio/ now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete pic...

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1 month ago

Apache Spark lineage now available in Amazon SageMaker Unified Studio for IDC based domains Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. Data Lineage provides you with the information you need to identify the root cause of complex issues and understand the impact of changes. This feature supports lineage capture of schema and transformations of data assets and columns from Spark executions in EMR-EC2, EMR-Serverless, EMR-EKS, and AWS Glue. You can then explore this lineage visually as a graph in SageMaker Unified Studio or query it using APIs. You can also use lineage to compare transformations across Spark job's history. Spark lineage is available in all existing SageMaker Unified Studio https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. For detailed information on how to get started with lineage using these new features, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/datazone-data-lineage-automate-capture-from-tools.html.

Apache Spark lineage now available in Amazon SageMaker Unified Studio for IDC based domains

Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. ...

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2 months ago

SageMaker Unified Studio adds support for cross-Region and IAM role-based subscriptions Amazon SageMaker Unified Studio now supports cross-Region subscriptions and IAM role-based subscriptions for simple and flexible data access and governance. With cross-Region support, you can subscribe to AWS Glue tables and views, as well as Amazon Redshift tables and views published in a different AWS Region than your project. This capability helps break down data silos and enable better collaboration across your organization by allowing teams to access curated data assets from any AWS Region without manual replication. Additionally, you can now discovery and request access to data through IAM-role based subscriptions. This allows you to request access to data without requiring a SageMaker Unified Studio project, eliminating the project intermediary layer and simplifying access to data through IAM roles. To get started with cross-Region subscriptions, log into SageMaker Unified Studio, or use the Amazon DataZone API, SDK, or AWS CLI. IAM role-based subscriptions are available via Amazon DataZone API and SDK. These new APIs for cross-Region subscriptions and IAM role-based access are available in all https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html is supported. To learn more, see the SageMaker Unified Studio https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/grant-access-to-unmanaged-asset.html.

SageMaker Unified Studio adds support for cross-Region and IAM role-based subscriptions

Amazon SageMaker Unified Studio now supports cross-Region subscriptions and IAM role-based subscriptions for simple and flexible data access and governance. With cross-Region su...

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2 months ago

SageMaker Unified Studio adds support for cross-Region and IAM role-based subscriptions Amazon SageMaker Unified Studio now supports cross-Region subscriptions and IAM role-based subscriptions for simple and flexible data access and governance. With cross-Region support, you can subscribe to AWS Glue tables and views, as well as Amazon Redshift tables and views published in a different AWS Region than your project. This capability helps break down data silos and enable better collaboration across your organization by allowing teams to access curated data assets from any AWS Region without manual replication. Additionally, you can now discovery and request access to data through IAM-role based subscriptions. This allows you to request access to data without requiring a SageMaker Unified Studio project, eliminating the project intermediary layer and simplifying access to data through IAM roles. To get started with cross-Region subscriptions, log into SageMaker Unified Studio, or use the Amazon DataZone API, SDK, or AWS CLI. IAM role-based subscriptions are available via Amazon DataZone API and SDK. These new APIs for cross-Region subscriptions and IAM role-based access are available in all AWS Regions where SageMaker Unified Studio is supported. To learn more, see the SageMaker Unified Studio user guide.

🆕 Amazon SageMaker Unified Studio now supports cross-Region and IAM role-based subscriptions for seamless data access and governance, breaking down silos and simplifying data discovery without manual replication, available in all supported regions.

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3 months ago

Amazon SageMaker now supports self-service migration of Notebook instances to latest platform versions Amazon SageMaker Notebook instance now supports self-service migration, allowing you to update your notebook instance platform identifier through the UpdateNotebookInstance API. This enables you to seamlessly transition from unsupported platform identifiers (notebook-al1-v1, notebook-al2-v1, notebook-al2-v2) to supported versions (notebook-al2-v3, notebook-al2023-v1). With the new https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateNotebookInstance.html#sagemaker-UpdateNotebookInstance-request-PlatformIdentifier parameter in the UpdateNotebookInstance API, you can update to newer versions of the Notebook instance platform while preserving your existing data and configurations. The https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-jl.html#nbi-jl-version-maintenance determines which Operating System and JupyterLab version combination your notebook instance runs. This self-service capability simplifies the migration process and helps you keep your notebook instances current. This feature is supported through AWS CLI (version 2.31.27 or newer) and SDK, and is available in all AWS Regions where Amazon SageMaker Notebook instances are supported. To learn more, see https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-update.html in the Amazon SageMaker Developer Guide.

Amazon SageMaker now supports self-service migration of Notebook instances to latest platform versions

Amazon SageMaker Notebook instance now supports self-service migration, allowing you to update your notebook instance platform identifier through t...

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3 months ago

Amazon SageMaker now supports self-service migration of Notebook instances to latest platform versions Amazon SageMaker Notebook instance now supports self-service migration, allowing you to update your notebook instance platform identifier through the UpdateNotebookInstance API. This enables you to seamlessly transition from unsupported platform identifiers (notebook-al1-v1, notebook-al2-v1, notebook-al2-v2) to supported versions (notebook-al2-v3, notebook-al2023-v1). With the new PlatformIdentifier parameter in the UpdateNotebookInstance API, you can update to newer versions of the Notebook instance platform while preserving your existing data and configurations. The platform identifier determines which Operating System and JupyterLab version combination your notebook instance runs. This self-service capability simplifies the migration process and helps you keep your notebook instances current. This feature is supported through AWS CLI (version 2.31.27 or newer) and SDK, and is available in all AWS Regions where Amazon SageMaker Notebook instances are supported. To learn more, see Update a Notebook Instance in the Amazon SageMaker Developer Guide.

🆕 Amazon SageMaker lets you migrate Notebook instances to latest versions easily, keeping data and settings intact, via UpdateNotebookInstance API. Available globally.

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4 months ago

Amazon SageMaker Unified Studio adds support for catalog notifications Amazon SageMaker Unified Studio now provides real-time notifications for data catalog activities, enabling data teams to stay informed of subscription requests, dataset updates, and access approvals. With this launch, customers receive real-time notifications for catalog events including new dataset publications, metadata changes, and access approvals directly within the SageMaker Unified Studio notification center. This launch streamlines collaboration by keeping teams updated as datasets are published or modified. The new notification experience in SageMaker Unified Studio is accessible from a “bell” icon in the top right corner of the project home page. From here, you can access a short list of recent notifications including subscription requests, updates, comments, and system events. To see the full list of all notifications, you can click on “notification center” to see all notifications in a tabular view that can be filtered based on your preferences for data catalogs, projects and event types. Notifications within SageMaker Unified Studio is available in all https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To learn more, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/sagemaker-events-and-notifications.html

Amazon SageMaker Unified Studio adds support for catalog notifications

Amazon SageMaker Unified Studio now provides real-time notifications for data catalog activities, enabling data teams to stay informed of subscription requests, dataset updates,...

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4 months ago

Amazon SageMaker Unified Studio adds support for catalog notifications Amazon SageMaker Unified Studio now provides real-time notifications for data catalog activities, enabling data teams to stay informed of subscription requests, dataset updates, and access approvals. With this launch, customers receive real-time notifications for catalog events including new dataset publications, metadata changes, and access approvals directly within the SageMaker Unified Studio notification center. This launch streamlines collaboration by keeping teams updated as datasets are published or modified. The new notification experience in SageMaker Unified Studio is accessible from a “bell” icon in the top right corner of the project home page. From here, you can access a short list of recent notifications including subscription requests, updates, comments, and system events. To see the full list of all notifications, you can click on “notification center” to see all notifications in a tabular view that can be filtered based on your preferences for data catalogs, projects and event types. Notifications within SageMaker Unified Studio is available in all regions where SageMaker Unified Studio is supported. To learn more, refer to the SageMaker Unified Studio guide.

🆕 Amazon SageMaker Unified Studio provides real-time notifications for data catalog activities, like subscription requests and updates, boosting collaboration and keeping teams informed directly in the notification center. Available globally.

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5 months ago

Amazon SageMaker adds additional search context for search results Amazon SageMaker enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. Users can see which metadata fields matched their query and understand why each result appears, increasing clarity and trust in data discovery. The capability introduces inline highlighting for matched terms and an explanation panel that details where and how each match occurred across metadata fields such as name, description, glossary, schema, and other metadata. The enhancement reduces time spent evaluating irrelevant assets by presenting match evidence directly in search results. Users can quickly validate relevance without opening individual assets. This capability is now available in all AWS Regions where Amazon SageMaker is supported. To learn more about Amazon SageMaker, see Amazon SageMaker https://docs.aws.amazon.com/next-generation-sagemaker/latest/userguide/what-is-sagemaker.html.

Amazon SageMaker adds additional search context for search results

Amazon SageMaker enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. Users can see which metadata fie...

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5 months ago

Amazon SageMaker adds additional search context for search results Amazon SageMaker enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. Users can see which metadata fields matched their query and understand why each result appears, increasing clarity and trust in data discovery. The capability introduces inline highlighting for matched terms and an explanation panel that details where and how each match occurred across metadata fields such as name, description, glossary, schema, and other metadata. The enhancement reduces time spent evaluating irrelevant assets by presenting match evidence directly in search results. Users can quickly validate relevance without opening individual assets. This capability is now available in all AWS Regions where Amazon SageMaker is supported. To learn more about Amazon SageMaker, see Amazon SageMaker documentaion.

🆕 Amazon SageMaker boosts search results with extra context, showing matched metadata for better transparency and relevance, reducing evaluation time. Available globally. For details, check Amazon SageMaker docs.

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5 months ago

Amazon SageMaker Unified Studio announces single sign-on support for interactive Spark sessions Amazon SageMaker Unified Studio announces corporate identity support for interactive Apache Spark sessions through AWS Identity Center’s trusted identity propagation. This new capability enables seamless single sign-on and end-to-end data access traceability for data analytics workflows. Data engineers and scientists can now access data resources in Apache Spark sessions in their JupyterLab environment using their organizational identities, while administrators can implement fine-grained access controls and maintain comprehensive audit trails. For data administrators, this feature simplifies security management using AWS Lake Formation, Amazon S3 Access Grants, and Amazon Redshift Data APIs, enabling centralized access controls across Amazon EMR on EC2, EMR on EKS, EMR Serverless, and AWS Glue. Organizations can define granular permissions based on identity provider credentials for Spark sessions and SageMaker Studio notebook flows, including training and processing jobs. This integration is complemented by comprehensive AWS CloudTrail logging of all user activities—from interactive JupyterLab sessions to https://docs.aws.amazon.com/singlesignon/latest/userguide/user-background-sessions.html - streamlining compliance monitoring and audit requirements. Identity support for Spark sessions in SageMaker Unified Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Seoul), and Asia Pacific (Tokyo). To learn more, visit the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/trusted-identity-propagation.html.

Amazon SageMaker Unified Studio announces single sign-on support for interactive Spark sessions

Amazon SageMaker Unified Studio announces corporate identity support for interactive Apache Spark sessions through AWS Identity Center’s trusted ident...

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5 months ago

Amazon SageMaker Unified Studio announces single sign-on support for interactive Spark sessions Amazon SageMaker Unified Studio announces corporate identity support for interactive Apache Spark sessions through AWS Identity Center’s trusted identity propagation. This new capability enables seamless single sign-on and end-to-end data access traceability for data analytics workflows. Data engineers and scientists can now access data resources in Apache Spark sessions in their JupyterLab environment using their organizational identities, while administrators can implement fine-grained access controls and maintain comprehensive audit trails. For data administrators, this feature simplifies security management using AWS Lake Formation, Amazon S3 Access Grants, and Amazon Redshift Data APIs, enabling centralized access controls across Amazon EMR on EC2, EMR on EKS, EMR Serverless, and AWS Glue. Organizations can define granular permissions based on identity provider credentials for Spark sessions and SageMaker Studio notebook flows, including training and processing jobs. This integration is complemented by comprehensive AWS CloudTrail logging of all user activities—from interactive JupyterLab sessions to user background sessions - streamlining compliance monitoring and audit requirements. Identity support for Spark sessions in SageMaker Unified Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Seoul), and Asia Pacific (Tokyo). To learn more, visit the SageMaker Unified Studio documentation.

🆕 Amazon SageMaker Unified Studio now supports single sign-on for Apache Spark sessions via AWS Identity Center, enabling seamless access and fine-grained controls, with logging available in multiple regions.

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6 months ago

Amazon SageMaker Unified Studio announces the general availability of the Custom Blueprints Today, AWS announced the general availability of Custom Blueprints, a new feature in Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature allows customers to use their own managed policies as per their corporate security requirements to create a project role in SageMaker Unified Studio. Customers can either replace the managed policies provide by Amazon SageMaker Unified Studio as part of the tooling blueprint with their custom policies or enrich the existing policies by appending additional policies. In addition to allowing you to bring your own managed policies, Custom Blueprints is designed to provide you the ability to configure the infrastructure and resources that you want to deploy in the project created in Amazon SageMaker Unified Studio. Using your own AWS CloudFormation templates you can define and customize the parameters and configuration for any AWS resources such as Amazon EMR on EC2, AWS Glue Data Catalog, and Amazon Redshift. You can replace the service managed blueprints with your custom blueprints in order to ensure standardization across your entire organization. The sample templates to create your custom blueprints are available https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/custom-blueprints.html. The ability to use Custom Blueprint is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html for more details. For instructions on how to get started, visit the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/custom-blueprints.html.

Amazon SageMaker Unified Studio announces the general availability of the Custom Blueprints

Today, AWS announced the general availability of Custom Blueprints, a new feature in Amazon SageMaker Unified Studio, part of the nex...

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6 months ago

Amazon SageMaker Unified Studio announces the general availability of the Custom Blueprints Today, AWS announced the general availability of Custom Blueprints, a new feature in Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature allows customers to use their own managed policies as per their corporate security requirements to create a project role in SageMaker Unified Studio. Customers can either replace the managed policies provide by Amazon SageMaker Unified Studio as part of the tooling blueprint with their custom policies or enrich the existing policies by appending additional policies. In addition to allowing you to bring your own managed policies, Custom Blueprints is designed to provide you the ability to configure the infrastructure and resources that you want to deploy in the project created in Amazon SageMaker Unified Studio. Using your own AWS CloudFormation templates you can define and customize the parameters and configuration for any AWS resources such as Amazon EMR on EC2, AWS Glue Data Catalog, and Amazon Redshift. You can replace the service managed blueprints with your custom blueprints in order to ensure standardization across your entire organization. The sample templates to create your custom blueprints are available here. The ability to use Custom Blueprint is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the supported regions list for more details. For instructions on how to get started, visit the Amazon SageMaker documentation.

🆕 AWS releases Custom Blueprints in Amazon SageMaker Unified Studio, enabling users to import managed policies and use CloudFormation templates for customized, region-standardized deployments.

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6 months ago

Amazon SageMaker Catalog adds support for governed classification with restricted terms Amazon SageMaker Catalog now supports governed classification through Restricted Classification Terms, allowing catalog administrators to control which users and projects can apply sensitive glossary terms to their assets. This new capability is designed to help organizations enforce metadata standards and ensure classification consistency across teams and domains. With this launch, glossary terms can be marked as "restricted", and only authorized users or groups—defined through explicit policies—can use them to classify data assets. For example, a centralized data governance team may define terms like “Seller-MCF” or “PII” that reflect data handling policies. These terms can now be governed so only specific project members (e.g., trusted admin groups) can apply them, which helps support proper control over how sensitive classifications are assigned. This feature is now available in https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html where Amazon SageMaker Unified Studio is supported. To get started and learn more about this feature, see SageMaker Unified Studio https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/create-maintain-business-glossary.html.

Amazon SageMaker Catalog adds support for governed classification with restricted terms

Amazon SageMaker Catalog now supports governed classification through Restricted Classification Terms, allowing catalog administrators to control which users an...

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6 months ago

Amazon SageMaker Catalog adds support for governed classification with restricted terms Amazon SageMaker Catalog now supports governed classification through Restricted Classification Terms, allowing catalog administrators to control which users and projects can apply sensitive glossary terms to their assets. This new capability is designed to help organizations enforce metadata standards and ensure classification consistency across teams and domains. With this launch, glossary terms can be marked as "restricted", and only authorized users or groups—defined through explicit policies—can use them to classify data assets. For example, a centralized data governance team may define terms like “Seller-MCF” or “PII” that reflect data handling policies. These terms can now be governed so only specific project members (e.g., trusted admin groups) can apply them, which helps support proper control over how sensitive classifications are assigned. This feature is now available in all AWS regions where Amazon SageMaker Unified Studio is supported. To get started and learn more about this feature, see SageMaker Unified Studio user guide.

🆕 Amazon SageMaker Catalog adds governed classification with restricted terms, letting admins control user access to sensitive glossary terms, ensuring metadata standards and consistency. Available in all regions with SageMaker Unified Studio.

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7 months ago

Amazon SageMaker introduces account-agnostic, reusable project profiles Amazon SageMaker introduces account-agnostic, reusable project profiles (templates) in Amazon SageMaker Unified Studio domain, enabling domain administrators to define project configurations once and reuse them across multiple AWS accounts and regions. Project profiles are no longer tied to a specific AWS account or region. Instead, platform teams can reference an account pool—a new domain entity that enables dynamic account and region selection at the time of project creation, based on custom enterprise authorization policies or user-specific logic. This decoupling of profile definitions from static deployment settings simplifies governance, reduces duplication, and accelerates onboarding across large-scale data and ML environments. Project creators benefit from a more flexible experience: during project creation, they can select from a personalized list of authorized AWS accounts and regions, powered by custom resolution strategies or predefined account pools. This model supports organizations operating across hundreds or thousands of accounts, while preserving centralized control and permission boundaries. This feature is now available in all https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html where Amazon SageMaker Unified Studio is supported. To learn more about account-agnostic project profiles in Amazon SageMaker refer to https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/account-pools.html.

Amazon SageMaker introduces account-agnostic, reusable project profiles

Amazon SageMaker introduces account-agnostic, reusable project profiles (templates) in Amazon SageMaker Unified Studio domain, enabling domain administrators to define project...

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7 months ago

Amazon SageMaker introduces account-agnostic, reusable project profiles Amazon SageMaker introduces account-agnostic, reusable project profiles (templates) in Amazon SageMaker Unified Studio domain, enabling domain administrators to define project configurations once and reuse them across multiple AWS accounts and regions. Project profiles are no longer tied to a specific AWS account or region. Instead, platform teams can reference an account pool—a new domain entity that enables dynamic account and region selection at the time of project creation, based on custom enterprise authorization policies or user-specific logic. This decoupling of profile definitions from static deployment settings simplifies governance, reduces duplication, and accelerates onboarding across large-scale data and ML environments. Project creators benefit from a more flexible experience: during project creation, they can select from a personalized list of authorized AWS accounts and regions, powered by custom resolution strategies or predefined account pools. This model supports organizations operating across hundreds or thousands of accounts, while preserving centralized control and permission boundaries. This feature is now available in all AWS Regions where Amazon SageMaker Unified Studio is supported. To learn more about account-agnostic project profiles in Amazon SageMaker refer to account pools in Amazon SageMaker Unified Studio.

🆕 Amazon SageMaker adds reusable project profiles for Unified Studio, centralizing configurations across AWS accounts and regions, easing governance and onboarding for large data and ML setups. Available everywhere.

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9 months ago

Upgrade Experience from Amazon SageMaker Studio to SageMaker Unified Studio Amazon SageMaker now offers an upgrade experience that enables customers to transition from SageMaker Studio to SageMaker Unified Studio while preserving their existing resources and maintaining consistent access controls. This new capability allows customers to import their SageMaker AI domains, user profiles, and spaces into SageMaker Unified Studio without redeploying infrastructure. The upgrade tool ensures that identity, authentication, and authorization experiences remain consistent, with users retaining access to only the resources they were previously permitted to use. With this upgrade experience, customers can continue to access their resources from both SageMaker Studio and SageMaker Unified Studio during the transition period, allowing teams to gradually adapt to the new experience. The tool preserves access to existing JupyterLab and CodeEditor spaces, as well as other SageMaker AI resources like training jobs, ML pipelines, models, inference endpoints etc, previously created from SageMaker Studio. Administrators maintain control over the upgrade process and can disable access to SageMaker Studio once users are comfortable with the SageMaker Unified Studio experience. The upgrade tool is available as an open-source solution that provides a guided, step-by-step process to ensure a smooth transition to SageMaker Unified Studio. The upgrade experience is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html for more details. To learn more about upgrading from SageMaker Studio to SageMaker Unified Studio, visit the https://github.com/aws/Unified-Studio-for-Amazon-Sagemaker/tree/main/migration/sagemaker-ai, and to learn more about the next generation of Amazon SageMaker, visit the https://aws.amazon.com/sagemaker/.

Upgrade Experience from Amazon SageMaker Studio to SageMaker Unified Studio

Amazon SageMaker now offers an upgrade experience that enables customers to transition from SageMaker Studio to SageMaker Unified Studio while preser...

#AWS #AmazonSagemakerStudio #AmazonMachineLearning #AmazonSagemaker

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9 months ago

Upgrade Experience from Amazon SageMaker Studio to SageMaker Unified Studio Amazon SageMaker now offers an upgrade experience that enables customers to transition from SageMaker Studio to SageMaker Unified Studio while preserving their existing resources and maintaining consistent access controls. This new capability allows customers to import their SageMaker AI domains, user profiles, and spaces into SageMaker Unified Studio without redeploying infrastructure. The upgrade tool ensures that identity, authentication, and authorization experiences remain consistent, with users retaining access to only the resources they were previously permitted to use. With this upgrade experience, customers can continue to access their resources from both SageMaker Studio and SageMaker Unified Studio during the transition period, allowing teams to gradually adapt to the new experience. The tool preserves access to existing JupyterLab and CodeEditor spaces, as well as other SageMaker AI resources like training jobs, ML pipelines, models, inference endpoints etc, previously created from SageMaker Studio. Administrators maintain control over the upgrade process and can disable access to SageMaker Studio once users are comfortable with the SageMaker Unified Studio experience. The upgrade tool is available as an open-source solution that provides a guided, step-by-step process to ensure a smooth transition to SageMaker Unified Studio. The upgrade experience is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the supported regions list for more details. To learn more about upgrading from SageMaker Studio to SageMaker Unified Studio, visit the GitHub repository, and to learn more about the next generation of Amazon SageMaker, visit the product detail page.

🆕 Amazon SageMaker's upgrade tool lets you transition from SageMaker Studio to Unified Studio, preserving resources, controls, and profiles. Available in all regions, it's gradual and open-source.

#AWS #AmazonSagemakerStudio #AmazonMachineLearning #AmazonSagemaker

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AWS News(Unofficial)

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10 months ago

Amazon SageMaker Unified Studio now allows you to bring your own image (BYOI) Today, AWS announced the ability to bring your own image (BYOI) to Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature benefits customers who have regulatory and compliance requirements or who prefer not to use the framework containers that come with the default SageMaker Distribution image. BYOI provides you the flexibility to customize the image by removing unnecessary frameworks and adding new dependencies or security containers as per your requirement. It also provides the code reproducibility guarantees on the containers that you use across development and production environements. The SageMaker Distribution image is available on https://github.com/aws/sagemaker-distribution you can download the image inspect its contents and use it to build your custom image. The base image contains all the necessary packages and extensions which are required to execute the code on SageMaker Unified Studio, therefore we recommend you to build your own image using the https://github.com/aws/sagemaker-distribution/blob/main/support_policy.md#supported-image-versions. The ability to bring your own images is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html for more details. For instructions on how to get started, visit the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/byoi.html.

Amazon SageMaker Unified Studio now allows you to bring your own image (BYOI)

Today, AWS announced the ability to bring your own image (BYOI) to Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker...

#AWS #AmazonSagemaker #AmazonMachineLearning #AmazonSagemakerStudio

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@awsrecentnews.bsky.social

10 months ago

Amazon SageMaker Unified Studio now allows you to bring your own image (BYOI) Today, AWS announced the ability to bring your own image (BYOI) to Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature benefits customers who have regulatory and compliance requirements or who prefer not to use the framework containers that come with the default SageMaker Distribution image. BYOI provides you the flexibility to customize the image by removing unnecessary frameworks and adding new dependencies or security containers as per your requirement. It also provides the code reproducibility guarantees on the containers that you use across development and production environements. The SageMaker Distribution image is available on GitHub, you can download the image inspect its contents and use it to build your custom image. The base image contains all the necessary packages and extensions which are required to execute the code on SageMaker Unified Studio, therefore we recommend you to build your own image using the SageMaker Distribution version 2.6 and onwards. The ability to bring your own images is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the supported regions list for more details. For instructions on how to get started, visit the Amazon SageMaker documentation.

🆕 AWS now supports BYOI in Amazon SageMaker Unified Studio for regulatory and custom needs. Available in all regions with next-gen SageMaker. For details, see documentation.

#AWS #AmazonSagemaker #AmazonMachineLearning #AmazonSagemakerStudio

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AWS News(Unofficial)

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11 months ago

Amazon SageMaker Studio now supports recovery mode for applications We are excited to announce that Amazon SageMaker Studio now supports recovery mode, enabling users to regain access to their JupyterLab and Code Editor applications when configuration issues prevent normal startup. Starting today, when users encounter application startup failures due to issues such as corrupted Conda configuration or insufficient storage space, they can launch their application in recovery mode on Studio UI or using AWS CLI. When configuration issues occur, users see a warning banner with the recommended solution and can choose to run their space in recovery mode. This simplified environment provides access to essential features like terminal and file explorer, allowing users to diagnose and fix configuration issues without administrator intervention. This functionality provides users with an important self-service mechanism, helping them minimize workspace downtime. This feature is available in all AWS Regions where Amazon SageMaker Studio is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, visit our https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-troubleshooting.html.

Amazon SageMaker Studio now supports recovery mode for applications

We are excited to announce that Amazon SageMaker Studio now supports recovery mode, enabling users to regain access to their JupyterLab and Code Editor applications when configuration issues preven...

#AWS #AmazonSagemakerStudio

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AWS News Feed on 🦋

@awsrecentnews.bsky.social

11 months ago

Amazon SageMaker Studio now supports recovery mode for applications We are excited to announce that Amazon SageMaker Studio now supports recovery mode, enabling users to regain access to their JupyterLab and Code Editor applications when configuration issues prevent normal startup. Starting today, when users encounter application startup failures due to issues such as corrupted Conda configuration or insufficient storage space, they can launch their application in recovery mode on Studio UI or using AWS CLI. When configuration issues occur, users see a warning banner with the recommended solution and can choose to run their space in recovery mode. This simplified environment provides access to essential features like terminal and file explorer, allowing users to diagnose and fix configuration issues without administrator intervention. This functionality provides users with an important self-service mechanism, helping them minimize workspace downtime. This feature is available in all AWS Regions where Amazon SageMaker Studio is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, visit our documentation.

🆕 Amazon SageMaker Studio now supports recovery mode for JupyterLab and Code Editor apps, allowing users to fix startup issues like corrupted Conda or storage problems, minimizing downtime. Available in all regions except China and GovCloud.

#AWS #AmazonSagemakerStudio

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