Today, we are excited to announce the general availability of anonymous user access for Amazon Q Business. This feature allows customers to create Q Business applications for anonymous users using publicly accessible content. Q Business applications created in this anonymous mode are billed on a API consumption basis.

Customers can now create anonymous Q Business applications to power use cases such as public web site Q&A, documentation portals, and customer self-service experiences, where user authentication is not required and content is publicly available. For example, AnyCompany wants to improve their website’s visitor support experience by providing a genAI assistant over their publicly available help/product pages. The customer would create an anonymous Q Business application and index all the public product help/documentation to power their Q Business genAI assistant. To deploy the anonymous application, customers can implement the anonymous Chat/ChatSync APIs for higher UX control or embed the built-in anonymous web experience via an iFrame. Anonymous applications are billed on API consumption basis, offering a scalable way to deploy Q Business generative AI experiences to large anonymous audiences.

The anonymous chat APIs and web experience are available in the US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Sydney) AWS Regions. For more information, please consult our documentation.
 

 

​Today, we are excited to announce the general availability of anonymous user access for Amazon Q Business. This feature allows customers to create Q Business applications for anonymous users using publicly accessible content. Q Business applications created in this anonymous mode are billed on a API consumption basis. Customers can now create anonymous Q Business applications to power use cases such as public web site Q&A, documentation portals, and customer self-service experiences, where user authentication is not required and content is publicly available. For example, AnyCompany wants to improve their website’s visitor support experience by providing a genAI assistant over their publicly available help/product pages. The customer would create an anonymous Q Business application and index all the public product help/documentation to power their Q Business genAI assistant. To deploy the anonymous application, customers can implement the anonymous Chat/ChatSync APIs for higher UX control or embed the built-in anonymous web experience via an iFrame. Anonymous applications are billed on API consumption basis, offering a scalable way to deploy Q Business generative AI experiences to large anonymous audiences. The anonymous chat APIs and web experience are available in the US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Sydney) AWS Regions. For more information, please consult our documentation.    

Amazon VPC Lattice introduces dual stack support for management API, enabling you to connect using Internet Protocol Version 6 (IPv6), Internet Protocol Version 4 (IPv4), or dual stack clients. Dual stack support is also available when the Amazon VPC Lattice management API endpoint is privately accessed from your Amazon Virtual Private Cloud (VPC) using AWS PrivateLink. Dual stack endpoints are made available on a new AWS DNS domain name. The existing Amazon VPC Lattice management API endpoints are maintained for backwards compatibility reasons.

Amazon VPC Lattice, an application networking service that simplifies connecting, securing, and monitoring service-to-service communication. You can use Amazon VPC Lattice to facilitate cross-account and cross-VPC connectivity, as well as application layer load balancing for your workloads. Whether the underlying compute types are instances, containers, or serverless, with Amazon VPC Lattice developers can work with native integration on the compute platform of their choice. With simultaneous support for both IPv4 and IPv6 clients on VPC Lattice endpoints, you are able to gradually transition from IPv4 to IPv6 based systems and applications, without needing to switch all over at once. This enables you to meet IPv6 compliance requirements and removes the need for expensive networking equipment to handle the address translation between IPv4 and IPv6.

To learn more, see the VPC Lattice user guide and IPv6 on AWS.

 

​Amazon VPC Lattice introduces dual stack support for management API, enabling you to connect using Internet Protocol Version 6 (IPv6), Internet Protocol Version 4 (IPv4), or dual stack clients. Dual stack support is also available when the Amazon VPC Lattice management API endpoint is privately accessed from your Amazon Virtual Private Cloud (VPC) using AWS PrivateLink. Dual stack endpoints are made available on a new AWS DNS domain name. The existing Amazon VPC Lattice management API endpoints are maintained for backwards compatibility reasons. Amazon VPC Lattice, an application networking service that simplifies connecting, securing, and monitoring service-to-service communication. You can use Amazon VPC Lattice to facilitate cross-account and cross-VPC connectivity, as well as application layer load balancing for your workloads. Whether the underlying compute types are instances, containers, or serverless, with Amazon VPC Lattice developers can work with native integration on the compute platform of their choice. With simultaneous support for both IPv4 and IPv6 clients on VPC Lattice endpoints, you are able to gradually transition from IPv4 to IPv6 based systems and applications, without needing to switch all over at once. This enables you to meet IPv6 compliance requirements and removes the need for expensive networking equipment to handle the address translation between IPv4 and IPv6.
To learn more, see the VPC Lattice user guide and IPv6 on AWS.  

Today, AWS Clean Rooms announces support for multiple collaboration members to receive analysis results from queries using Spark SQL. This streamlined capability enhances usability and transparency by eliminating the need for additional audit mechanisms outside of the collaboration. With this feature, multiple members can receive and validate analysis results from queries across collective datasets directly from the collaboration.

You can designate multiple collaborators as result receivers when executing a Spark SQL query. Results are automatically delivered to all selected collaborators who are configured in both the collaboration settings and table controls. For example, in a collaboration between a media publisher and an advertiser, the publisher can run a query across their collective datasets; the query results are sent to both parties’ chosen Amazon S3 location for validation.

AWS Clean Rooms helps companies and their partners to 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.

 

​Today, AWS Clean Rooms announces support for multiple collaboration members to receive analysis results from queries using Spark SQL. This streamlined capability enhances usability and transparency by eliminating the need for additional audit mechanisms outside of the collaboration. With this feature, multiple members can receive and validate analysis results from queries across collective datasets directly from the collaboration. You can designate multiple collaborators as result receivers when executing a Spark SQL query. Results are automatically delivered to all selected collaborators who are configured in both the collaboration settings and table controls. For example, in a collaboration between a media publisher and an advertiser, the publisher can run a query across their collective datasets; the query results are sent to both parties’ chosen Amazon S3 location for validation. AWS Clean Rooms helps companies and their partners to 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 Connect now provides bulk removal of agent schedules, making day-to-day management of agent schedules more efficient. With this launch, you can now remove schedules for up to 400 agents for a single day, or up to 30 days for a single agent. For example, remove all schedules for next Monday as the contact center is going to be closed, or remove future shifts for an agent who is no longer with the organization. With bulk remove, managers no longer have to remove agent shifts one agent and one day at a time, thus improving manager productivity by reducing time spent on managing agent schedules.

This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To learn more about Amazon Connect agent scheduling, click here.

 

​Amazon Connect now provides bulk removal of agent schedules, making day-to-day management of agent schedules more efficient. With this launch, you can now remove schedules for up to 400 agents for a single day, or up to 30 days for a single agent. For example, remove all schedules for next Monday as the contact center is going to be closed, or remove future shifts for an agent who is no longer with the organization. With bulk remove, managers no longer have to remove agent shifts one agent and one day at a time, thus improving manager productivity by reducing time spent on managing agent schedules. This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To learn more about Amazon Connect agent scheduling, click here.  

Starting today, we’re enhancing the AWS Migration Acceleration Program (MAP) with two key capabilities to help you accelerate your modernization efforts and drive customers’ adoption of AI:

• New “Move to AI” Modernization Pathway, featuring Amazon Bedrock and Amazon SageMaker. This pathway enables you to help customers transform their existing applications and business processes with proven AI patterns that deliver measurable business value.
• Amazon Connect is now a qualifying service in the MAP Modernization Strategic Partner Incentive (SPI). This enables you to help customers transform their contact centers with AI-powered features that increase agent productivity and enhance customer experiences.

These enhancements strengthen your ability to lead customers’ AI transformation and drive contact center modernization.

Learn more:

• AWS Partner Funding Benefits Program Guide
• MAP Modernization SPI Eligible Services

 

​Starting today, we’re enhancing the AWS Migration Acceleration Program (MAP) with two key capabilities to help you accelerate your modernization efforts and drive customers’ adoption of AI:

New “Move to AI” Modernization Pathway, featuring Amazon Bedrock and Amazon SageMaker. This pathway enables you to help customers transform their existing applications and business processes with proven AI patterns that deliver measurable business value.
Amazon Connect is now a qualifying service in the MAP Modernization Strategic Partner Incentive (SPI). This enables you to help customers transform their contact centers with AI-powered features that increase agent productivity and enhance customer experiences.

These enhancements strengthen your ability to lead customers’ AI transformation and drive contact center modernization. Learn more:

AWS Partner Funding Benefits Program Guide
MAP Modernization SPI Eligible Services  

EC2 Image Builder now integrates with Systems Manager Parameter Store, offering customers a streamlined approach for referencing SSM parameters in their image recipes, components, and distribution configurations. This capability allows customers to dynamically select base images within their image recipes, easily use configuration data and sensitive information for components, and update their SSM parameters with output latest images.

Prior to today, customers had to specify AMI IDs in their image recipes to use custom base images, leading to a constant maintenance cycle when these base images had to be updated. Furthermore, customers were required to create custom scripts to update SSM parameters with output images and to utilize SSM parameter values in components, resulting in substantial operational overhead. Now, customers can leverage SSM Parameters as inputs for their image recipes, enabling them to dynamically retrieve the latest base image. This integration extends to components, where SSM Parameters can be easily referenced to save, retrieve and use sensitive information in components, and to the distribution process, where SSM parameters can be updated with latest output images. These enhancements streamline the image building workflow, reduce manual intervention and improve overall efficiency.

This capability is available to all customers at no additional costs, and is enabled in all AWS commercial regions including AWS GovCloud (US), AWS China (Beijing) Region, operated by Sinnet, and in the AWS China (Ningxia) Region, operated by NWCD.

Customers can get started from the EC2 Image Builder Console, CLI, API, CloudFormation, or CDK, and learn more in the EC2 Image Builder documentation.
 

 

​EC2 Image Builder now integrates with Systems Manager Parameter Store, offering customers a streamlined approach for referencing SSM parameters in their image recipes, components, and distribution configurations. This capability allows customers to dynamically select base images within their image recipes, easily use configuration data and sensitive information for components, and update their SSM parameters with output latest images. Prior to today, customers had to specify AMI IDs in their image recipes to use custom base images, leading to a constant maintenance cycle when these base images had to be updated. Furthermore, customers were required to create custom scripts to update SSM parameters with output images and to utilize SSM parameter values in components, resulting in substantial operational overhead. Now, customers can leverage SSM Parameters as inputs for their image recipes, enabling them to dynamically retrieve the latest base image. This integration extends to components, where SSM Parameters can be easily referenced to save, retrieve and use sensitive information in components, and to the distribution process, where SSM parameters can be updated with latest output images. These enhancements streamline the image building workflow, reduce manual intervention and improve overall efficiency. This capability is available to all customers at no additional costs, and is enabled in all AWS commercial regions including AWS GovCloud (US), AWS China (Beijing) Region, operated by Sinnet, and in the AWS China (Ningxia) Region, operated by NWCD. Customers can get started from the EC2 Image Builder Console, CLI, API, CloudFormation, or CDK, and learn more in the EC2 Image Builder documentation.    

Today, Amazon Kinesis Data Streams introduces support for tagging and Attribute-Based Access Control (ABAC) for enhanced fan-out consumers. You can register enhanced fan-out consumers to have dedicated low latency read throughput per shard, up to 2MB/s. ABAC is an authorization strategy that defines access permissions based on tags that can be attached to IAM users, roles, and AWS resources for fine-grained access control. This new feature enables you to apply tags for allocating costs and simplifying permission management for your enhanced fan-out consumers.

With this launch, you can now tag your enhanced fan-out consumers used by different business units to track and allocate costs in AWS Cost Explorer without manually tracking costs per consumer. You can apply tags to enhanced fan-out consumers using the Kinesis Data Streams API or AWS Command Line Interface (CLI). Additionally, ABAC support for enhanced fan-out consumers allows you to use IAM policies to allow or deny specific Kinesis Data Streams API actions when the IAM principal’s tags match the tags on a registered consumer.

Tagging and Attribute-Based Access Control for enhanced fan-out consumers are available in all AWS Regions, including the AWS China and AWS GovCloud (US) Regions. To learn more about tagging and ABAC support for consumers, see Tag your resources and Attribute-Based Access Control (ABAC) for AWS.
 

 

​Today, Amazon Kinesis Data Streams introduces support for tagging and Attribute-Based Access Control (ABAC) for enhanced fan-out consumers. You can register enhanced fan-out consumers to have dedicated low latency read throughput per shard, up to 2MB/s. ABAC is an authorization strategy that defines access permissions based on tags that can be attached to IAM users, roles, and AWS resources for fine-grained access control. This new feature enables you to apply tags for allocating costs and simplifying permission management for your enhanced fan-out consumers. With this launch, you can now tag your enhanced fan-out consumers used by different business units to track and allocate costs in AWS Cost Explorer without manually tracking costs per consumer. You can apply tags to enhanced fan-out consumers using the Kinesis Data Streams API or AWS Command Line Interface (CLI). Additionally, ABAC support for enhanced fan-out consumers allows you to use IAM policies to allow or deny specific Kinesis Data Streams API actions when the IAM principal’s tags match the tags on a registered consumer. Tagging and Attribute-Based Access Control for enhanced fan-out consumers are available in all AWS Regions, including the AWS China and AWS GovCloud (US) Regions. To learn more about tagging and ABAC support for consumers, see Tag your resources and Attribute-Based Access Control (ABAC) for AWS.    

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• IA
• Salud
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The post Invertir en agentes de cambio del estado de Washington: Conozcan a los ganadores de la convocatoria abierta del AI for Good Lab appeared first on Source LATAM.

 

​The post Invertir en agentes de cambio del estado de Washington: Conozcan a los ganadores de la convocatoria abierta del AI for Good Lab appeared first on Source LATAM.  

Amazon Relational Database Service (Amazon RDS) on AWS Outposts now supports generation 7i instances for Amazon RDS for MySQL on Outposts and amazon RDS for PostgreSQL on Outposts. Amazon RDS on Outposts allows you to deploy fully managed database instances in your on-premises environments. AWS Outposts is a fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any datacenter, co-location space, or on-premises facility for a truly consistent hybrid experience. You can deploy Amazon RDS on Outposts to set up, operate, and scale MySQL, Microsoft SQL Server and PostgreSQL relational databases on-premises, just as you would in the cloud.

Amazon RDS on Outposts now support Generation 7i instances in Asia Pacific (Singapore), Canada (Central), Europe (Frankfurt, Ireland, London, Milan, Paris, Spain, Stockholm), US East (N. Virginia, Ohio), US West (N. California, Oregon) regions. For more information about Amazon RDS on Outposts, visit our product page, our documentation, or get started now.

 

​Amazon Relational Database Service (Amazon RDS) on AWS Outposts now supports generation 7i instances for Amazon RDS for MySQL on Outposts and amazon RDS for PostgreSQL on Outposts. Amazon RDS on Outposts allows you to deploy fully managed database instances in your on-premises environments. AWS Outposts is a fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any datacenter, co-location space, or on-premises facility for a truly consistent hybrid experience. You can deploy Amazon RDS on Outposts to set up, operate, and scale MySQL, Microsoft SQL Server and PostgreSQL relational databases on-premises, just as you would in the cloud. Amazon RDS on Outposts now support Generation 7i instances in Asia Pacific (Singapore), Canada (Central), Europe (Frankfurt, Ireland, London, Milan, Paris, Spain, Stockholm), US East (N. Virginia, Ohio), US West (N. California, Oregon) regions. For more information about Amazon RDS on Outposts, visit our product page, our documentation, or get started now.  

Today, AWS announced Amazon EKS Hybrid Nodes support for Bottlerocket, the Linux-based operating system purpose-built for containers. EKS Hybrid Nodes unifies Kubernetes management across cloud, on-premises, and edge environments by enabling customers to use their on-premises infrastructure as nodes in EKS clusters. Customers can now use Bottlerocket as the node operating system for hybrid nodes running in VMware vSphere environments.

EKS Hybrid Nodes customers get the security and efficiency benefits of the Bottlerocket operating system purpose-built for containers and supported by AWS. Customers can now use the same Bottlerocket operating system with EKS across their cloud and on-premises environments to further strengthen their operational consistency.

EKS Hybrid Nodes supports VMware variants of Bottlerocket versions 1.37 and newer across all AWS Regions where EKS Hybrid Nodes is available. These variants support Kubernetes versions 1.28 and above. To get started, see the Amazon EKS User Guide.

 

​Today, AWS announced Amazon EKS Hybrid Nodes support for Bottlerocket, the Linux-based operating system purpose-built for containers. EKS Hybrid Nodes unifies Kubernetes management across cloud, on-premises, and edge environments by enabling customers to use their on-premises infrastructure as nodes in EKS clusters. Customers can now use Bottlerocket as the node operating system for hybrid nodes running in VMware vSphere environments. EKS Hybrid Nodes customers get the security and efficiency benefits of the Bottlerocket operating system purpose-built for containers and supported by AWS. Customers can now use the same Bottlerocket operating system with EKS across their cloud and on-premises environments to further strengthen their operational consistency. EKS Hybrid Nodes supports VMware variants of Bottlerocket versions 1.37 and newer across all AWS Regions where EKS Hybrid Nodes is available. These variants support Kubernetes versions 1.28 and above. To get started, see the Amazon EKS User Guide.