This setup allows you to build, train, deploy, and serve the spam classifier entirely on AWS in a...

Learn how Salesforce used Amazon SageMaker AI Inference Component placement (the SchedulingConfig parameter) to distribute model copies across multiple Availability Zones, meeting their Multi-AZ high availability compliance requirements without sacrificing the cost efficiency of multi-model co-hosting.

Learn how ZS built a security-hardened Amazon SageMaker platform that balances developer agility with healthcare-grade governance, serving 1,000+ daily active users across 200+ SageMaker domains.

Podcast AWS LATAM · Episode

Amazon EMR on EKS now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with Spark running on the Amazon EKS clusters they already operate.   An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark runs on your Amazon EKS cluster. This architecture supports workflows including ad hoc data exploration and incremental PySpark job development before deploying to production. Each session runs as pods on a virtual cluster, secured with your AWS Identity and Access Management (IAM) execution role and tagged by project and user. Spark Connect on Amazon EMR on EKS is available with EMR release 7.14 (Apache Spark 3.5) and emr-spark-8.1.0 (Apache Spark 4.1), in all AWS Commercial Regions. The Amazon SageMaker Unified Studio experience is available in supported AWS Regions. To get started, visit the Spark Connect on Amazon EMR on EKS documentation or the Amazon SageMaker Unified Studio Getting Started guide.

Amazon EMR on EKS now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with Spark running on the Amazon EKS clusters they already operate.   An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark runs on your Amazon EKS cluster. This architecture supports workflows including ad hoc data exploration and incremental PySpark job development before deploying to production. Each session runs as pods on a virtual cluster, secured with your AWS Identity and Access Management (IAM) execution role and tagged by project and user. Spark Connect on Amazon EMR on EKS is available with EMR release 7.14 (Apache Spark 3.5) and emr-spark-8.1.0 (Apache Spark 4.1), in all AWS Commercial Regions. The Amazon SageMaker Unified Studio experience is available in https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To get started, visit the https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks-spark-connect.html or the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/notebooks-spark-connect.html#spark-connect-emr-eks

We are pleased to announce general availability of Amazon EC2 P6-B200 instances in AWS US East (N. Virginia) on SageMaker notebook instances. 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 and SageMaker notebook instances.

Amazon SageMaker Feature Store now supports two new APIs: BatchWriteRecord writes up to 25 records across multiple feature groups in a single call, and ListRecords enumerates record identifiers within a feature group. In this post, we walk through each API with code examples you can use to get started.

The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.

AWS continues to expand access to the most advanced foundation models with OpenAI open weight models now available in Amazon Bedrock and Amazon SageMaker JumpStart. Accessing these new models from OpenAI on AWS, gpt-oss-120b and gpt-oss-20b, gives you more freedom to innovate and choose the right model for your specific use cases while maintaining complete control over your infrastructure and data.

Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.

Today, AWS announces remote connection from Cursor IDE to Amazon SageMaker Unified Studio via the AWS Toolkit extension. This new capability allows data scientists, ML engineers, and developers to leverage their Cursor setup - including its AI-powered code completion, natural language editing, and multi-file editing capabilities - while accessing the scalable compute resources of Amazon SageMaker. By connecting Cursor to SageMaker Unified Studio using the AWS Toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing AI-assisted development workflows within a single environment for all your AWS analytics and AI/ML services. SageMaker Unified Studio, part of the next generation of Amazon SageMaker, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open-Source Software). Starting today, you can also use your customized local Cursor setup - complete with custom rules, extensions, and AI model preferences - while accessing your compute resources and data on Amazon SageMaker. Since Cursor is built on Code-OSS, authentication is secure via IAM through the AWS Toolkit extension, giving you access to all your SageMaker Unified Studio domains and projects. This integration provides a convenient path from your local AI-powered development environment to scalable infrastructure for running workloads across data processing, SQL analytics services like Amazon EMR, AWS Glue, and Amazon Athena, and ML workflows - all with enterprise-grade security including customer-managed encryption keys and AWS IAM integration. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the local IDE support documentation..

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering […]

Amazon SageMaker Catalog now supports metadata enforcement rules for glossary terms, requiring data producers to apply approved business vocabulary when publishing assets. This helps consistent data classification and improves discoverability across organizational catalogs. This new capability allows administrators to define mandatory glossary term requirements for data assets during the publishing workflow. Data producers must now classify their assets with approved business terms from organizational glossaries before publication, ensuring consistent metadata standards and improving data discoverability. The enforcement rules validate that required glossary terms are applied, preventing assets from being published without proper business context. By standardizing metadata and aligning technical data schemas with business language, this capability enhances data governance, improves search relevance, and helps business users more easily understand and trust published data assets. Metadata enforcement rules for glossary terms are available in all AWS regions where Amazon SageMaker Catalog operates. To get started, visit the Amazon SageMaker console and navigate to the Catalog governance section to configure glossary term enforcement policies. You can also use the AWS CLI or SDKs to programmatically manage metadata rules for asset publishing.  To learn more about Amazon SageMaker Catalog, visit the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/metadata-rules-publishing.html.

Today, AWS announces remote connection from Visual Studio Code (VS Code) to Amazon SageMaker Unified Studio. This new capability allows developers to leverage their VS Code setup while accessing the scalable compute resources of Amazon SageMaker. By connecting VS Code to SageMaker Unified Studio, you can maintain your existing development workflows and configurations within a unified environment for AWS analytics and AI/ML services. SageMaker Unified Studio, part of the next generation of Amazon SageMaker, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open Source Software) like VS Code. Starting today, you can use your customized local VS Code setup while accessing your compute resources and data in Amazon SageMaker. Authentication is simple and secure using the AWS Toolkit extension in VS Code. This integration provides a streamlined path from your local development environment to scalable infrastructure for running data processing, SQL analytics, and ML workflows. This feature is available in all Regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/what-is-sagemaker-unified-studio.html and https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html. 

Amazon SageMaker Unified Studio now provides domain management experience for Identity Center and IAM-based domains outside of AWS console, allows administrators and data management teams to create and manage projects, configure workforce identity, manage users and permissions, and set networking properties for projects. Previously, this was only available for IAM based domains. With this launch, administrators of Identity Center-based domains can access domain management capabilities in SageMaker Unified Studio portal to create projects with configurable execution roles that define which AWS analytics, AI, and ML services the project can access. VPC configuration is consistent across both domain types, inherited by all projects, and can be edited to change the VPC, subnets, or security group. Administrators can also manage associated accounts, enabling users to publish and consume data from other AWS accounts within SageMaker Unified Studio. These features are available in all https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html is available. To learn more, visit the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/access-domain-admin-portal-idc.html. 

Amazon SageMaker AI Inference now supports bidirectional streaming for real-time speech-to-text transcription, enabling continuous speech processing instead of batch input. Models can now receive audio streams and return partial transcripts simultaneously as users speak, enabling you to build voice agents that process speech with minimal latency. As customers build AI voice agents, they need real-time speech transcription to minimize delays between user speech and agent responses. Data scientists and ML engineers lack managed infrastructure for bidirectional streaming, making it necessary to build custom WebSocket implementations and manage streaming protocols. Teams spend weeks developing and maintaining this infrastructure rather than focusing on model accuracy and agent capabilities. With bidirectional streaming on Amazon SageMaker AI Inference, you can deploy speech-to-text models by invoking your endpoint with the new Bidirectional Stream API. The client opens an HTTP2 connection to the SageMaker AI runtime, and SageMaker AI automatically creates a WebSocket connection to your container. This can process streaming audio frames and return partial transcripts as they are produced. Any container implementing a WebSocket handler following the SageMaker AI contract works automatically, with real-time speech models such as Deepgram running without modifications. This eliminates months of infrastructure development, enabling you to deploy voice agents with continuous transcription while focusing your time on improving model performance. Bidirectional streaming is available in following AWS Regions - Canada (Central), South America (São Paulo), Africa (Cape Town), Europe (Paris), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Israel (Tel Aviv), Europe (Zurich), Asia Pacific (Tokyo), AWS GovCloud US (West), AWS GovCloud US (East), Asia Pacific (Mumbai), Middle East (Bahrain), US West (Oregon), China (Ningxia), US West (Northern California), Asia Pacific (Sydney), Europe (London), Asia Pacific (Seoul), US East (N. Virginia), Asia Pacific (Hong Kong), US East (Ohio), China (Beijing), Europe (Stockholm), Europe (Ireland), Middle East (UAE), Asia Pacific (Osaka), Asia Pacific (Melbourne), Europe (Spain), Europe (Frankfurt), Europe (Milan), Asia Pacific (Singapore). To learn more, visit AWS News Blog here and SageMaker AI documentation here.

Amazon SageMaker introduces one-click onboarding of existing AWS datasets to Amazon SageMaker Unified Studio. This helps AWS customers to start working with their data in minutes, using their existing AWS Identity and Access Management (IAM) roles and permissions. Customers can start working with any data they have access to using a new serverless notebook with a built-in AI agent. This new notebook, which supports SQL, Python, Spark or natural language, gives data engineers, analysts, and data scientists a single high-performance interface to develop and run both SQL queries and code. Customers also have access to many other existing tools such as a Query Editor for SQL analysis, JupyterLab IDE, Visual ETL and workflows, and machine learning (ML) capabilities. The ML capabilities include the ability to discover foundation models from a centralized model hub, customize them with sample notebooks, use MLflow for experimentation, publish trained models in the model hub for discovery, and deploy them as inference endpoints for prediction. Customers can start directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, and Amazon S3 Tables console pages, giving them a fast path from their existing tools and data to the simple experience in SageMaker Unified Studio. After clicking ‘Get started’ and specifying an IAM role, SageMaker prompts for specific policy updates and then automatically creates a project in SageMaker Unified Studio. The project is set up with all existing data permissions from AWS Glue Data Catalog, AWS Lake Formation, and Amazon S3, and a notebook and serverless compute are pre-configured to accelerate first use. To get started, simply click "Get Started" from the SageMaker console or open SageMaker Unified Studio from Amazon Athena, Amazon Redshift, or Amazon S3 Tables. One-click onboarding of existing datasets is available in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more read the AWS News Blog or visit the Amazon SageMaker documentation.

Amazon SageMaker introduces a built-in AI agent that accelerates the development of data analytics and machine learning (ML) applications. SageMaker Data Agent is available in the new notebook experience in Amazon SageMaker Unified Studio and helps data engineers, analysts, and data scientists who spend significant time on manual setup tasks and boilerplate code when building analytics and ML applications. The agent generates code and execution plans from natural language prompts and integrates with data catalogs and business metadata to streamline the development process. SageMaker Data Agent works within the new notebook experience to break down complex analytics and ML tasks into manageable steps. Customers can describe objectives in natural language and the agent creates a detailed execution plan and generates the required SQL and Python code. The agent maintains awareness of the notebook context, including available data sources and catalog information, accelerating common tasks including data transformation, statistical analysis, and model development. To get started, log in to Amazon SageMaker and click on “Notebooks” on the left navigation. Amazon SageMaker Data Agent is available in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more, read the AWS News Blog or visit the Amazon SageMaker documentation.