Amazon SageMaker AI now supports IAM Identity Center (IdC) multi-region replication, enabling you to deploy SageMaker AI Studio domains in different regions from your IdC instance's primary region. Previously, the primary IdC instance had to reside in the same region as the SageMaker AI Studio domain; now customers can replicate their primary IdC instance into the region where they wish to use SageMaker AI Studio domains. This capability helps customers maintain compliance while utilizing centralized workforce identity management. As an Amazon SageMaker AI Studio administrator, you can deploy SageMaker AI domains that align with your workforce's location and data residency requirements, independent of where your primary IdC instance is set up. Organizations can retain their primary IdC instance in one region while operating SageMaker AI Studio workloads in compliance-mandated regions, support global operations through centralized identity management, and satisfy data sovereignty requirements without compromising single sign-on (SSO) capabilities. To get started see the https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-custom.html and to learn about setting up IAM Identity Center multi-region support see the https://docs.aws.amazon.com/singlesignon/latest/userguide/multi-region-iam-identity-center.html. Multi-region IdC replication is available in all standard https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Amazon SageMaker AI Studio is supported, including: Asia Pacific (Tokyo), Europe (Ireland), US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), South America (São Paulo), Asia Pacific (Seoul), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Mumbai), Europe (Paris), Europe (Stockholm).

Amazon SageMaker AI now supports IAM Identity Center (IdC) multi-region replication, enabling you to deploy SageMaker AI Studio domains in different regions from your IdC instance's primary region. Previously, the primary IdC instance had to reside in the same region as the SageMaker AI Studio domain; now customers can replicate their primary IdC instance into the region where they wish to use SageMaker AI Studio domains. This capability helps customers maintain compliance while utilizing centralized workforce identity management. As an Amazon SageMaker AI Studio administrator, you can deploy SageMaker AI domains that align with your workforce's location and data residency requirements, independent of where your primary IdC instance is set up. Organizations can retain their primary IdC instance in one region while operating SageMaker AI Studio workloads in compliance-mandated regions, support global operations through centralized identity management, and satisfy data sovereignty requirements without compromising single sign-on (SSO) capabilities. To get started see the SageMaker AI documentation and to learn about setting up IAM Identity Center multi-region support see the IAM Identity Center User Guide. Multi-region IdC replication is available in all standard AWS Regions where Amazon SageMaker AI Studio is supported, including: Asia Pacific (Tokyo), Europe (Ireland), US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), South America (São Paulo), Asia Pacific (Seoul), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Mumbai), Europe (Paris), Europe (Stockholm).

Amazon SageMaker Studio now lets new customers sign up with their Google, GitHub, or Apple credentials and land inside a fully configured Studio environment - no traditional AWS account setup, no credit card, and no console navigation required. Previously, new customers who discovered a model on Hugging Face or an AWS product page had to complete a multi-step AWS account creation process, provide a credit card, navigate the AWS Console to find SageMaker AI, configure an environment, and set up IAM permissions before they could begin working. With earlier launches, we reduced environment setup to twenty seconds, pre-configured model customization permissions, and introduced deep links from Hugging Face and AWS product pages so customers could land directly inside Studio with a model pre-loaded. Now, with social sign-in, new customers complete a simplified sign-up using an existing identity, receive free credits as part of the AWS Free Tier with no credit card required, and arrive inside a ready-to-use SageMaker Studio environment where they can immediately fine-tune, evaluate, and deploy foundation models. Customers can invite team members by email, and collaborators sign in with their own social credentials to access the shared Studio environment, including shared models, notebooks, and endpoints without configuring IAM users or permissions. When a project grows beyond free credits, customers can upgrade to a paid plan and set a spend limit to stay within budget. Verified customers continue to receive default GPU access to G5, G6, G6e, and G4dn instances across endpoint deployments, training jobs, and notebooks without requesting quota increases. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, visit any supported model on https://huggingface.co/models and select "Customize and Deploy on SageMaker AI" or "Deploy on SageMaker AI," or click Get Started from the https://aws.amazon.com/sagemaker/ai/studio/ page. To learn more, see the https://docs.aws.amazon.com/accounts/latest/reference/sign-in-new.html and https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html.

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 CICD-for-SageMakerUnifiedStudio repository on GitHub. For more information, see the CI/CD for Amazon SageMaker Unified Studio documentation.

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.

Amazon SageMaker AI now offers Generative AI Inference Recommendations in SageMaker AI Studio, giving customers a guided, low-code, no-code path to find the best inference configuration for their workload. This builds on the API-based launch in April 2026, extending the same benchmarking infrastructure to teams that prefer a visual workflow over programmatic access. Deploying generative AI models in production requires finding the right combination of instance type, serving container, and optimization strategy. Getting this right typically involves weeks of manual benchmarking, configuration tuning, and trial-and-error, with no easy way to know if the final setup is actually optimal. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. In SageMaker AI Studio under Jobs, Inference optimization, customers select a use-case profile (Interact, Generate, Summarize, or Custom), choose an optimization goal (minimize latency, maximize throughput, or minimize cost), and pick their model from JumpStart, S3, Model Registry, or an existing SageMaker model. Recommendations are ranked by TTFT, inter-token latency, throughput, and cost, and can be compared visually before deploying to a SageMaker real-time endpoint directly from Studio. There is no additional cost for generating recommendations. Standard compute costs apply for optimization jobs and endpoints provisioned during benchmarking. This capability is available in US East (N. Virginia), US West (Oregon), US East (Ohio), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Tokyo). To learn more, visit the blog post or the documentation.

Amazon SageMaker AI now offers Generative AI Inference Recommendations in SageMaker AI Studio, giving customers a guided, low-code, no-code path to find the best inference configuration for their workload. This builds on the API-based launch in April 2026, extending the same benchmarking infrastructure to teams that prefer a visual workflow over programmatic access. Deploying generative AI models in production requires finding the right combination of instance type, serving container, and optimization strategy. Getting this right typically involves weeks of manual benchmarking, configuration tuning, and trial-and-error, with no easy way to know if the final setup is actually optimal. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. In SageMaker AI Studio under Jobs, Inference optimization, customers select a use-case profile (Interact, Generate, Summarize, or Custom), choose an optimization goal (minimize latency, maximize throughput, or minimize cost), and pick their model from JumpStart, S3, Model Registry, or an existing SageMaker model. Recommendations are ranked by TTFT, inter-token latency, throughput, and cost, and can be compared visually before deploying to a SageMaker real-time endpoint directly from Studio. There is no additional cost for generating recommendations. Standard compute costs apply for optimization jobs and endpoints provisioned during benchmarking. This capability is available in US East (N. Virginia), US West (Oregon), US East (Ohio), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Tokyo). To learn more, visit the https://aws.amazon.com/blogs/machine-learning/launching-ui-for-generative-ai-inference-recommendations-in-amazon-sagemaker-ai/ or the https://docs.aws.amazon.com/sagemaker/latest/dg/generative-ai-inference-recommendations.html.

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 […]

AmazonSageMakerStudioを使用していてユーザープロファイルを開こうとするとたまに以下のエラーメッセージが出てJupyterNotebookが開かないことがありますTh

AmazonSageMakerStudioを使用していてユーザープロファイルを開こうとするとたまに以下のエラーメッセージが出てJupyterNotebookが開かないことがありますTh

We are pleased to announce general availability of Amazon EC2 P6-B200 instances in AWS US East (N. Virginia) on SageMaker Studio notebooks. https://aws.amazon.com/ec2/instance-types/p6/ 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 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.

Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly. As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html and https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-open-weight-prereq.html in the Amazon SageMaker documentation.

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.

Amazon SageMaker Studio now supports direct integration from Hugging Face, letting you go from discovering a model to working with it inside a fully configured Studio environment in a single click. Select any supported model on Hugging Face and choose "Customize on SageMaker AI" or "Deploy on SageMaker AI" to land directly on the corresponding workflow page with the model pre-loaded and ready to use. Previously, getting from model discovery to a working environment required navigating the AWS Console to find SageMaker AI, configuring an environment, setting up IAM permissions for serverless model customization, and in many cases requesting GPU quota increases through Service Quotas before running a first job. Now, new customers complete a standard AWS sign-up and receive a SageMaker Studio environment created in seconds with pre-configured permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. Verified customers receive default GPU access to G5, G6, and G4dn instances across endpoint deployments, training jobs, and notebooks without requesting quota increases, and quota limit and utilization information is visible for each instance type directly inside the Studio environment. Returning customers signing in from Hugging Face or SageMaker product pages select their environment and land directly inside SageMaker Studio with the model ready to use. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, visit any supported model on https://huggingface.co/models and select "Customize on SageMaker AI" or "Deploy on SageMaker AI," or click Get Started from the https://aws.amazon.com/sagemaker/ai/studio/ page. To learn more, see https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-launch.html in the Amazon SageMaker documentation.

https://aws.amazon.com/ec2/instance-types/g7/, powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, deliver up to 4.6x AI inference performance compared to previous-generation G6 instances. Whether you are deploying conversational AI assistants, scaling video streaming, or running large scale data processing pipelines, G7 gives you the performance you need. With up to 8 GPUs and 700 Gbps of EFA-enabled network bandwidth, G7 instances help you run AI inference, graphics, and data analytics workloads more efficiently. Custom Intel Xeon 6 processors, 7x EFA-enabled bandwidth and 1.5x higher FP16 Flops than G6, enable you to deploy models with lower latency for applications like conversational assistants, content generation tools, and recommendation engines. Amazon EC2 G7 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 P4de instances in Asia Pacific (Tokyo, Singapore) and Europe (Frankfurt) on SageMaker Studio notebooks. https://aws.amazon.com/ec2/instance-types/p4/ are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, 2X higher than the GPUs in our current P4d instances. The new P4de instances provide a total of 640GB of GPU memory, which provide up to 60% better ML training performance along with 20% lower cost to train when compared to P4d instances. The improved performance will allow customers to reduce model training times and accelerate time to market. Increased GPU memory on P4de will also benefit workloads that need to train on large datasets of high-resolution data. 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. 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. 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 G6e instances in the Middle East (Dubai), Asia Pacific (Tokyo, Seoul) and Europe (Frankfurt, Stockholm, Spain) on SageMaker Studio notebooks. https://aws.amazon.com/ec2/instance-types/g6e/ are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio. 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 P5.4xl 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.

Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code. With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. To learn more, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/operators.html. To get started, see https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/serverless-visual-workflows.html in the Amazon SageMaker Unified Studio User Guide.

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 […]

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

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 […]