A humanoid robot has been appointed as chief marketing officer. Uri, the robot, had its first public outing at the Harvard v Brown football match last Saturday, drawing large crowds  …

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Amazon Managed Streaming for Apache Kafka  (Amazon MSK) has added support for Express brokers in eight additional AWS Regions: AWS GovCloud (US-West), AWS GovCloud (US-East), Asia Pacific (Jakarta), Asia Pacific (Melbourne), Asia Pacific (Osaka), Europe (Zurich), Israel (Tel Aviv), and Asia Pacific (Hong Kong).

Express brokers are a new broker type for Amazon MSK Provisioned designed to deliver up to 3x more throughput per broker, scale up to 20x faster, and reduce recovery time by 90% as compared to standard Apache Kafka brokers. Express brokers come pre-configured with Kafka best practices by default, support all Kafka APIs, and provide the same low-latency performance that Amazon MSK customers expect, so they can continue using existing client applications without any changes.

You can now create an MSK cluster with Express brokers in these AWS Regions from the Amazon MSK console . To get started visit, the MSK product page , pricing page , and the Amazon MSK Developer Guide .

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 .

AWS Parallel Computing Service (AWS PCS) now enables you to modify and update key Slurm workload manager settings without rebuilding your cluster. You can now adjust essential parameters including accounting configurations and workload management settings on existing clusters, where previously these details were fixed at creation time.

This new flexibility helps you adapt your high performance computing (HPC) environment to changing requirements without disrupting operations. You can make modifications through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK.

AWS PCS is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. You can use AWS PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. AWS PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure.

Cluster configuration modifications are available in all AWS Regions where AWS PCS is offered . To learn more, see the Modifying a cluster section in the AWS PCS User Guide.

Amazon Bedrock now offers Cohere Embed v4, the latest state-of-the-art multimodal embedding model from Cohere that produces high-quality embeddings for text, images, and complex business documents. This powerful addition to Amazon Bedrock enables enterprises to build AI applications with frontier search and retrieval capabilities.

Traditional embedding models often struggle to understand complex multimodal business materials, such as business presentations and sales and financial reports, requiring extensive data pre-processing pipelines. Embed v4 addresses this challenge by natively processing documents with tables, graphs, diagrams, code snippets, and even handwritten notes. The model handles real-world imperfections such as spelling errors and formatting issues, eliminating the need for time-consuming data cleanup and helping you surface insights from previously difficult-to-search information.

With support for over 100 languages, including Arabic, English, French, Japanese, and Korean, Embed v4 enables global organizations to seamlessly search for information, breaking language barriers. The model is also fine-tuned for industries such as finance, healthcare, and manufacturing, delivering superior performance on specialized documents including financial reports, medical records, and product specifications.

Cohere Embed v4 is available for on-demand inference in US East (N. Virginia), Europe (Ireland), and Asia Pacific (Tokyo), and can be accessed from select public AWS Regions through cross-region inference. Review the Amazon Bedrock Model Support by Regions guide for complete regional availability. To get started, visit the Amazon Bedrock console to request model access. For more information, refer to the Cohere product page and documentation .

AWS Clean Rooms now supports data access budgets for tables associated to a collaboration. This new privacy control allows you to limit the number of times your data can be analyzed when training or running inference on a custom ML model or in a SQL query or Pyspark job. With data access budgets, you can establish per-period budgets that refresh daily, weekly, or monthly, lifetime budgets for overall usage, or both types simultaneously. When you spend a budget, the system prevents additional analyses until the budget refreshes, but you can reset or edit a budget at anytime as your needs change.

AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms .

Amazon Elastic Container Services (Amazon ECS), a fully managed container orchestration service, now provides one-click event capture and event history querying directly in the AWS Management Console.

ECS lifecycle events, including service action events and task state changes, provide visibility into your ECS environment for monitoring and troubleshooting. The ECS console now enables you to set up event capture with a single click, and access an intuitive query interface without leaving the ECS console. The ECS console automatically creates and manages the underlying EventBridge rules and CloudWatch log groups, while providing pre-built query templates and filters for common troubleshooting scenarios. Event capture through the ECS console is a great way to retain data for stopped tasks and recent events beyond the default limits.

To enable event capture, on the ECS console, navigate to the Cluster details page, locate the Configuration tab, and click the “Turn on event capture” button. Once enabled, navigate to the Event history tab on your cluster or service details page to query and analyze historical events. The console provides commonly used query parameters such as time range, task ID, and deployment ID, along with filters for stop codes and container exit codes. You can view detailed task lifecycle events such as task state changes and service action events without needing to navigate to another console, or use query languages.

ECS console event capture is available in all AWS Commercial Regions and AWS GovCloud (US) Regions. To learn more, visit the ECS developer guide .

AWS Parallel Computing Service (AWS PCS) now offers expanded Slurm configuration capabilities, enabling you to set over 60 additional parameters for granular control over your high performance computing (HPC) cluster operations. This enhancement provides more flexibility in managing job scheduling, resource allocation, access control, and job lifecycle.

The new Slurm custom settings give you fine-grained control over various resource management scenarios, including fair-share scheduling and quality of service levels. For example, you can now implement queue-specific priority policies, configure preemption settings, and set custom time and resource limits. Additionally, you can control access permissions at the account level and configure per-job execution behaviors. These and other capabilities help you to run a production HPC environment that efficiently serves multiple teams, projects, and workload types.

AWS PCS is a managed service that makes it easier for you to run and scale your HPC workloads and build scientific and engineering models on AWS using Slurm. You can use AWS PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. AWS PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure.

Expanded Slurm custom settings are available in all AWS Regions where AWS PCS is available . To learn more, see the AWS PCS User Guide .

AWS Parallel Computing Service (PCS) now allows you to reboot compute nodes using Slurm commands without triggering instance replacement. With this feature, you can reboot nodes for operational reasons such as troubleshooting, resource cleanup, and recovery from degraded states before requiring full node replacement, enabling you to efficiently maintain cluster health at lower costs.

This feature is available in all AWS Regions where PCS is available . You can use the ‘scontrol reboot’ command with options to schedule immediate or deferred reboots, while reboots through other methods will continue to trigger instance replacement. To learn more, refer to Rebooting compute nodes with Slurm in AWS PCS .

PCS is a managed service that simplifies running and scaling high performance computing (HPC) workloads on AWS using Slurm. To learn more about PCS, refer to the service documentation .

Starting today, Amazon EC2 Instance Connect Endpoint supports Internet Protocol Version 6 (IPv6) connectivity. Customers can now configure EIC Endpoints to be dual-stack or IPv6 only to connect to instances with IPv6 addresses.

EC2 Instance Connect helps customers have SSH and RDP connectivity to their EC2 instances without using public IP addresses. With IPv6 support, customers can establish connectivity to instances in private subnets using IPv6 addresses, while maintaining backwards compatibility with existing instances and subnets using IPv4 addresses.

EC2 Instance Connect Endpoint IPv6 support is available in all AWS Commercial Regions, the AWS GovCloud (US) Regions, and the China Regions. Customers can access their private EC2 instances through AWS EC2 Console, AWS CLI, and SSH clients. To learn more about connecting to EC2 instances using EC2 Instance Connect Endpoint, see documentation . To learn more about IPv6 support on AWS, visit the documentation page.