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 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 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 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 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 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 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/.  

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 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 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 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 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.

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.

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 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 Unified Studio now offers the CI/CD CLI (aws-smus-cicd-cli), an open-source command line tool that automates deployment of multi-service data and AI applications across development, test, and production. Organizations building applications in SageMaker Unified Studio combine multiple AWS services, including AWS Glue, Amazon Athena, Amazon MWAA, Amazon SageMaker AI, Amazon Bedrock, and Amazon QuickSight, into single applications. The CLI allows data teams to define applications once in a YAML manifest while DevOps teams deploy with a single command, reducing deployment bottlenecks and configuration drift. The CLI reads a declarative manifest.yaml that maps each pipeline stage to an isolated SageMaker Unified Studio project. At deploy time, it substitutes stage-specific configurations (S3 paths, IAM roles, account IDs, and connection strings) and provisions resources in dependency order. Four commands cover the lifecycle: describe validates permissions and connections, bundle packages an immutable artifact from the source target, deploy writes that artifact to the destination target, and test runs post-deployment validation. It works with existing CI/CD solutions such as GitHub Actions, Jenkins, and GitLab CI. The CI/CD CLI is available at no additional cost in all AWS Regions where Amazon SageMaker Unified Studio is available. You pay only for the underlying AWS resources provisioned during deployment. To get started, visit the following resources: The CI/CD CLI is available at no additional cost in all AWS Regions where Amazon SageMaker Unified Studio is available. You pay only for the underlying AWS resources provisioned during deployment. To get started, visit the following resources: Install from PyPI  GitHub repository Amazon SageMaker Unified Studio documentation

Amazon SageMaker Unified Studio now offers the CI/CD CLI (aws-smus-cicd-cli), an open-source command line tool that automates deployment of multi-service data and AI applications across development, test, and production. Organizations building applications in SageMaker Unified Studio combine multiple AWS services, including AWS Glue, Amazon Athena, Amazon MWAA, Amazon SageMaker AI, Amazon Bedrock, and Amazon QuickSight, into single applications. The CLI allows data teams to define applications once in a YAML manifest while DevOps teams deploy with a single command, reducing deployment bottlenecks and configuration drift. The CLI reads a declarative manifest.yaml that maps each pipeline stage to an isolated SageMaker Unified Studio project. At deploy time, it substitutes stage-specific configurations (S3 paths, IAM roles, account IDs, and connection strings) and provisions resources in dependency order. Four commands cover the lifecycle: describe validates permissions and connections, bundle packages an immutable artifact from the source target, deploy writes that artifact to the destination target, and test runs post-deployment validation. It works with existing CI/CD solutions such as GitHub Actions, Jenkins, and GitLab CI. The CI/CD CLI is available at no additional cost in all AWS Regions where Amazon SageMaker Unified Studio is available. You pay only for the underlying AWS resources provisioned during deployment. To get started, visit the following resources: The CI/CD CLI is available at no additional cost in all AWS Regions where Amazon SageMaker Unified Studio is available. You pay only for the underlying AWS resources provisioned during deployment. To get started, visit the following resources: https://pypi.org/project/aws-smus-cicd-cli/  https://github.com/aws/CICD-for-SageMakerUnifiedStudio https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/cicd.html

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.

We are pleased to announce general availability of Amazon EC2 P5.4xl instances on SageMaker Studio notebooks. https://aws.amazon.com/ec2/instance-types/p5/ are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition. Amazon EC2 P5.4xl instances are available for SageMaker Studio notebooks in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Mumbai, Tokyo, Jakarta) and South America (São Paulo) regions. Visit developer guides for instructions on setting up and using https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-jl.html and https://docs.aws.amazon.com/sagemaker/latest/dg/code-editor.html applications on https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated.html. For pricing information on these instances, please visit our https://aws.amazon.com/sagemaker/ai/pricing/?refid=ft_sagemaker.

We are pleased to announce general availability of Amazon EC2 P5.48xl instances in the AWS US West (San Francisco), Asia Pacific (Tokyo, Mumbai, Sydney, Jakarta) and Europe (London, Stockholm) regions on SageMaker Studio notebooks. Amazon EC2 P5.48xl instances are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition. Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio. For pricing information on these instances, please visit our pricing page.

https://aws.amazon.com/ec2/instance-types/g7e/ feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of Elastic Fabric Adapter networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFAv4 in EC2 UltraClusters, reducing latency for small-scale multi-node workloads. Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. Amazon EC2 G7e instances are available for SageMaker Studio notebooks in the AWS US East (N. Virginia and Ohio) and US West (Oregon) regions. Visit developer guides for instructions on setting up and using https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-jl.html and https://docs.aws.amazon.com/sagemaker/latest/dg/code-editor.html applications on https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated.html. For pricing information on these instances, please visit our https://aws.amazon.com/sagemaker/ai/pricing/?refid=ft_sagemaker.

We are pleased to announce general availability of Amazon EC2 P6-B200 instances in AWS US East (N. Virginia) on SageMaker Studio notebooks. Amazon EC2 P6-B200 instances are powered by 8 NVIDIA Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and 5th Generation Intel Xeon processors (Emerald Rapids). These instances deliver up to 2x better performance compared to P5en instances for AI training. Customers can use P6-B200 instances to interactively develop and fine-tune large foundation models, including LLMs, mixture of experts models, and multi-modal reasoning models. These instances enable efficient experimentation with larger models directly in JupyterLab or CodeEditor environments for generative AI applications such as enterprise copilots and content generation across text, images, and video. Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio. For pricing information on these instances, please visit our pricing page.

Amazon SageMaker Unified Studio now supports ODBC connections, so data users can connect Microsoft Power BI and other ODBC-compatible tools directly to governed data in SageMaker Unified Studio projects using the Amazon Athena ODBC driver. This adds a native connection path for analysts who have standardized on Power BI to work with governed data using their existing tools and workflows. Amazon SageMaker Unified Studio adds an ODBC connection details view to the project overview page, alongside the existing JDBC view. From the project's JDBC and ODBC connections tab, data users can copy the connection string and its parameters for a DSN-less connection, or use them to configure a named DSN. The Amazon Athena ODBC driver (version 2.2.0.1 or later) works directly with SageMaker Unified Studio through two modes: SageMakerBrowserIdc, which opens a browser to sign in through AWS IAM Identity Center and your external identity provider, and SageMakerIam, which uses temporary AWS credentials. ODBC connections in SageMaker Unified Studio are available today in all AWS Regions where Amazon SageMaker Unified Studio is available, at no additional cost. To get started, refer to the Amazon SageMaker Unified Studio User Guide and the Amazon Athena ODBC driver documentation.

Amazon SageMaker Unified Studio now supports ODBC connections, so data users can connect Microsoft Power BI and other ODBC-compatible tools directly to governed data in SageMaker Unified Studio projects using the Amazon Athena ODBC driver. This adds a native connection path for analysts who have standardized on Power BI to work with governed data using their existing tools and workflows. Amazon SageMaker Unified Studio adds an ODBC connection details view to the project overview page, alongside the existing JDBC view. From the project's JDBC and ODBC connections tab, data users can copy the connection string and its parameters for a DSN-less connection, or use them to configure a named DSN. The Amazon Athena ODBC driver (version 2.2.0.1 or later) works directly with SageMaker Unified Studio through two modes: SageMakerBrowserIdc, which opens a browser to sign in through AWS IAM Identity Center and your external identity provider, and SageMakerIam, which uses temporary AWS credentials. ODBC connections in SageMaker Unified Studio are available today in all AWS Regions where Amazon SageMaker Unified Studio is available, at no additional cost. To get started, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/query-with-odbc.html and the https://docs.aws.amazon.com/athena/latest/ug/odbc-v2-driver.html.

Amazon SageMaker Unified Studio CI/CD expands its open-source deployment toolkit with two new capabilities: (1) an AI agent skill that automates manifest authoring, and (2) native notebook promotion across environments. Together, they help data teams go from project to production faster while maintaining best-practice defaults across stages. AI-assisted manifest generation. The new generate-bundle-manifest agent skill inspects a project's connections, storage, and workflows and produces a ready-to-use deployment manifest. It applies least-privilege IAM guidance, substitutes environment variables in place of hardcoded resource identifiers, and sets safe defaults such as opt-in catalog handling. Teams can import the skill into their own agents to standardize how they package and promote SageMaker Unified Studio projects across development, test, and production accounts. Native notebook promotion. The CI/CD toolkit now supports promoting native SMUS Notebooks alongside code, workflows, and catalog assets. Notebook promotion uses an in-place synchronization model that creates a notebook on first deployment and updates it on subsequent deployments, preserving run history across releases. Teams can promote every notebook in a project or select specific notebooks by ID, and a dry-run mode validates S3 connectivity, IAM permissions, and notebook counts before deployment. Notebook promotion integrates with the existing bundle, deploy, destroy, and dry-run commands and requires no changes to current pipeline structure. Both capabilities are open source and available in all AWS Regions where Amazon SageMaker Unified Studio is offered. To get started, visit the https://github.com/aws/CICD-for-SageMakerUnifiedStudio repository on GitHub. For more information, see the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/cicd.html.