The post El nuevo Copilot for Gaming tiene como objetivo ahorrarles tiempo y ayudarlos a ser buenos appeared first on Source LATAM.

​The post El nuevo Copilot for Gaming tiene como objetivo ahorrarles tiempo y ayudarlos a ser buenos appeared first on Source LATAM.  

Amazon Bedrock RAG evaluation is now generally available. You can evaluate your retrieval-augmented generation (RAG) applications, either those built on Amazon Bedrock Knowledge Bases or a custom RAG system. You can evaluate either retrieval or end-to-end generation. Evaluations are powered by an LLM-as-a-judge, with a choice of several judge models. For retrieval, you can select from metrics such as context relevance and coverage. For end-to-end retrieve and generation, you can select from quality metrics such as correctness, completeness, and faithfulness (hallucination detection), and responsible AI metrics such as harmfulness, answer refusal, and stereotyping. You can also compare across evaluation jobs to iterate on your Knowledge Bases or custom RAG applications with different settings like chunking strategy or vector length, rerankers, or different content generating models.

*Brand new – more flexibility!* As of today, in addition to Bedrock Knowledge Bases, Amazon Bedrock’s RAG evaluations supports custom RAG pipeline evaluations. Customers evaluating custom RAG pipelines can now bring their input-output pairs and retrieved contexts into the evaluation job directly in their input dataset, enabling them to bypass the call to a Bedrock Knowledge Base («bring your own inference responses»). We also added citation precision and citation coverage metrics for Bedrock Knowledge Bases evaluation. If you use a Bedrock Knowledge Base as part of your evaluation, you can incorporate Amazon Bedrock Guardrails directly.

To learn more, visit the Amazon Bedrock Evaluations page and documentation. To get started, log into the Amazon Bedrock Console or use the Amazon Bedrock APIs.
 

​Amazon Bedrock RAG evaluation is now generally available. You can evaluate your retrieval-augmented generation (RAG) applications, either those built on Amazon Bedrock Knowledge Bases or a custom RAG system. You can evaluate either retrieval or end-to-end generation. Evaluations are powered by an LLM-as-a-judge, with a choice of several judge models. For retrieval, you can select from metrics such as context relevance and coverage. For end-to-end retrieve and generation, you can select from quality metrics such as correctness, completeness, and faithfulness (hallucination detection), and responsible AI metrics such as harmfulness, answer refusal, and stereotyping. You can also compare across evaluation jobs to iterate on your Knowledge Bases or custom RAG applications with different settings like chunking strategy or vector length, rerankers, or different content generating models. *Brand new – more flexibility!* As of today, in addition to Bedrock Knowledge Bases, Amazon Bedrock’s RAG evaluations supports custom RAG pipeline evaluations. Customers evaluating custom RAG pipelines can now bring their input-output pairs and retrieved contexts into the evaluation job directly in their input dataset, enabling them to bypass the call to a Bedrock Knowledge Base («bring your own inference responses»). We also added citation precision and citation coverage metrics for Bedrock Knowledge Bases evaluation. If you use a Bedrock Knowledge Base as part of your evaluation, you can incorporate Amazon Bedrock Guardrails directly. To learn more, visit the Amazon Bedrock Evaluations page and documentation. To get started, log into the Amazon Bedrock Console or use the Amazon Bedrock APIs.    

Today we’re excited to announce Research and Engineering Studio (RES) on AWS Version 2025.03. This release introduces the RES cost dashboard, supports custom instance lists by software stack, extends hibernation support to Linux virtual desktops, and supports virtual desktops running Windows 10 and 11.

Administrators now have access to the RES cost dashboard, which provides an overview of the Virtual Desktop Infrastructure (VDI) costs at a project level. Use the cost dashboard to get an overview of each project’s budget progress and view data related to historical spend.

RES 2024.08 introduced the ability to modify the list of allowable VDI instance types at the environment level. This release refines that feature by allowing administrators to assign any subset of allowed instances to specific software stacks. Assigning these software stacks to projects makes it possible to limit the instances available for VDIs at a project level.

RES 2025.03 also extends hibernation support to all supported Linux distributions and now supports launching VDIs with Windows 10 and 11. Software stacks now have an additional setting to launch VDIs with either shared, dedicated instance, or dedicated host tenancy to meet licensing requirements. Finally, the ability to create a software stack from a running session has returned. Use this as an alternative to EC2 Image Builder to streamline creation of custom Software Stacks and software images.

See the regional availability page for the list of regions where RES is available.

Check out additional release notes on Github to get started and deploy RES 2025.03.
 

​Today we’re excited to announce Research and Engineering Studio (RES) on AWS Version 2025.03. This release introduces the RES cost dashboard, supports custom instance lists by software stack, extends hibernation support to Linux virtual desktops, and supports virtual desktops running Windows 10 and 11. Administrators now have access to the RES cost dashboard, which provides an overview of the Virtual Desktop Infrastructure (VDI) costs at a project level. Use the cost dashboard to get an overview of each project’s budget progress and view data related to historical spend. RES 2024.08 introduced the ability to modify the list of allowable VDI instance types at the environment level. This release refines that feature by allowing administrators to assign any subset of allowed instances to specific software stacks. Assigning these software stacks to projects makes it possible to limit the instances available for VDIs at a project level. RES 2025.03 also extends hibernation support to all supported Linux distributions and now supports launching VDIs with Windows 10 and 11. Software stacks now have an additional setting to launch VDIs with either shared, dedicated instance, or dedicated host tenancy to meet licensing requirements. Finally, the ability to create a software stack from a running session has returned. Use this as an alternative to EC2 Image Builder to streamline creation of custom Software Stacks and software images. See the regional availability page for the list of regions where RES is available. Check out additional release notes on Github to get started and deploy RES 2025.03.    

Amazon Bedrock Model Evaluation’s LLM-as-a-judge capability is now generally available. Amazon Bedrock Model Evaluation allows you to evaluate, compare, and select the right models for your use case. You can choose an LLM as your judge from several available on Bedrock to ensure you have the right combination of evaluator models and models being evaluated. You can select quality metrics such as correctness, completeness, and professional style and tone, as well as responsible AI metrics such as harmfulness and answer refusal. You can evaluate all available models on Amazon Bedrock, including serverless models, Bedrock Marketplace models compatible with Converse API, customized and distilled models, imported models, and model routers. You can also compare results across evaluation jobs.

*Brand new – more flexibility!* Today, you can evaluate any model or system hosted anywhere by bringing your own inference responses you already fetched into your input prompt dataset for the evaluation job (“bring your own inference responses“). These responses can be from an Amazon Bedrock model or from any model or application hosted outside of Amazon Bedrock, enabling you to bypass calling an Amazon Bedrock model in the evaluation job, and allowing you to incorporate all the intermediate steps of your application into your final responses.

With LLM-as-a-judge, you can get human-like evaluation quality at lower cost, while saving weeks of time.

To learn more, visit the Amazon Bedrock Evaluations page and documentation. To get started, sign in to the AWS Console or use Amazon Bedrock APIs.
 

​Amazon Bedrock Model Evaluation’s LLM-as-a-judge capability is now generally available. Amazon Bedrock Model Evaluation allows you to evaluate, compare, and select the right models for your use case. You can choose an LLM as your judge from several available on Bedrock to ensure you have the right combination of evaluator models and models being evaluated. You can select quality metrics such as correctness, completeness, and professional style and tone, as well as responsible AI metrics such as harmfulness and answer refusal. You can evaluate all available models on Amazon Bedrock, including serverless models, Bedrock Marketplace models compatible with Converse API, customized and distilled models, imported models, and model routers. You can also compare results across evaluation jobs. *Brand new – more flexibility!* Today, you can evaluate any model or system hosted anywhere by bringing your own inference responses you already fetched into your input prompt dataset for the evaluation job (“bring your own inference responses“). These responses can be from an Amazon Bedrock model or from any model or application hosted outside of Amazon Bedrock, enabling you to bypass calling an Amazon Bedrock model in the evaluation job, and allowing you to incorporate all the intermediate steps of your application into your final responses. With LLM-as-a-judge, you can get human-like evaluation quality at lower cost, while saving weeks of time. To learn more, visit the Amazon Bedrock Evaluations page and documentation. To get started, sign in to the AWS Console or use Amazon Bedrock APIs.    

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Mexico (Central) and Asia Pacific (Thailand) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications.

With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs.

To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.

​Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Mexico (Central) and Asia Pacific (Thailand) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.  

Amazon Braket, the quantum computing service from AWS, now offers IonQ’s latest 36-qubit Forte Enterprise quantum processing unit (QPU) in the US East (N. Virginia) Region. This new device joins IonQ’s existing quantum hardware portfolio on Braket, which includes Forte-1, Aria-1, and Aria-2, providing customers with additional capacity to run their quantum workloads on ion-trapped devices.

With this launch, customers can use the familiar Braket SDK and APIs to access Forte Enterprise, which maintains the same capabilities that customers value in Forte-1. The device features IonQ’s debiasing and sharpening error mitigation algorithms to enable advanced customers workloads. Forte Enterprise continues to use the native ZZ gate architecture, making it easy for customers to seamlessly migrate workloads between the Forte devices.

IonQ Forte Enterprise is physically located in Switzerland, but all customer traffic routes through US East (N. Virginia) region. Customers can access this new device using the ARN: arn:aws:braket:us-east-1::device/qpu/ionq/Forte-Enterprise-1.

To get started with IonQ Forte Enterprise, visit the Amazon Braket devices page in the AWS Management Console to explore device specifications and capabilities. For additional guidance, review the Amazon Braket documentation and pricing information to make the most of this new device.

​Amazon Braket, the quantum computing service from AWS, now offers IonQ’s latest 36-qubit Forte Enterprise quantum processing unit (QPU) in the US East (N. Virginia) Region. This new device joins IonQ’s existing quantum hardware portfolio on Braket, which includes Forte-1, Aria-1, and Aria-2, providing customers with additional capacity to run their quantum workloads on ion-trapped devices. With this launch, customers can use the familiar Braket SDK and APIs to access Forte Enterprise, which maintains the same capabilities that customers value in Forte-1. The device features IonQ’s debiasing and sharpening error mitigation algorithms to enable advanced customers workloads. Forte Enterprise continues to use the native ZZ gate architecture, making it easy for customers to seamlessly migrate workloads between the Forte devices. IonQ Forte Enterprise is physically located in Switzerland, but all customer traffic routes through US East (N. Virginia) region. Customers can access this new device using the ARN: arn:aws:braket:us-east-1::device/qpu/ionq/Forte-Enterprise-1. To get started with IonQ Forte Enterprise, visit the Amazon Braket devices page in the AWS Management Console to explore device specifications and capabilities. For additional guidance, review the Amazon Braket documentation and pricing information to make the most of this new device.  

Today, AWS announces a new flow management feature for AWS Network Firewall that enables customers to identify and control active network flows. This feature introduces two key functions: Flow Capture, which allows point-in-time snapshots of active flows, and Flow Flush, which enables selective termination of specific connections. With these new capabilities, customers can now view and manage active flows based on criteria such as source/destination IP addresses, ports, and protocols, providing enhanced control over their network traffic.

This new feature helps customers maintain consistent security policies when updating firewall rules and enables rapid response during security incidents. Network administrators can now easily validate security configurations and ensure that all traffic is evaluated against current policies. The flow management feature is particularly valuable for troubleshooting network issues and isolating suspicious traffic during security events. By providing granular control over active network flows, AWS Network Firewall enhances customers’ ability to maintain a secure and efficient network environment.

The new flow management feature is available in all regions where AWS Network Firewall is supported, allowing customers to benefit from these enhanced capabilities across their global infrastructure.

Customers can start using Flow Capture and Flow Flush at no additional cost. To get started, visit the AWS Network Firewall documentation, explore the new APIs in the Network Firewall API Reference guide, or learn more about AWS Network Firewall on the product page.
 

​Today, AWS announces a new flow management feature for AWS Network Firewall that enables customers to identify and control active network flows. This feature introduces two key functions: Flow Capture, which allows point-in-time snapshots of active flows, and Flow Flush, which enables selective termination of specific connections. With these new capabilities, customers can now view and manage active flows based on criteria such as source/destination IP addresses, ports, and protocols, providing enhanced control over their network traffic. This new feature helps customers maintain consistent security policies when updating firewall rules and enables rapid response during security incidents. Network administrators can now easily validate security configurations and ensure that all traffic is evaluated against current policies. The flow management feature is particularly valuable for troubleshooting network issues and isolating suspicious traffic during security events. By providing granular control over active network flows, AWS Network Firewall enhances customers’ ability to maintain a secure and efficient network environment. The new flow management feature is available in all regions where AWS Network Firewall is supported, allowing customers to benefit from these enhanced capabilities across their global infrastructure. Customers can start using Flow Capture and Flow Flush at no additional cost. To get started, visit the AWS Network Firewall documentation, explore the new APIs in the Network Firewall API Reference guide, or learn more about AWS Network Firewall on the product page.    

Starting today, Amazon Q Business is available in AWS Europe region (Ireland). Amazon Q Business revolutionizes the way that employees interact with organizational knowledge and enterprise systems. Q Business customers in this region can get answers from enterprise RAG knowledge bases and uploaded files (e.g. pdf’s, images) and run tabular search on small tables. Customers can also get answers from LLM knowledge and generate content using their Q Business assistant. Amazon Q Business connects seamlessly to over 40 popular enterprise systems, including Amazon Simple Storage Service (Amazon S3), Microsoft 365, and Salesforce. It ensures that users access content securely with their existing credentials using single sign-on, according to their permissions, and enterprise-level access controls.

With this regional expansion, Amazon Q is now available in the following regions: US East (N. Virginia), US West (Oregon) and Europe West (Ireland) AWS Regions.

To learn more about the Amazon Q Business features available in this region, go to Q Business service regions.

For more information, see Amazon Q Business.

​Starting today, Amazon Q Business is available in AWS Europe region (Ireland). Amazon Q Business revolutionizes the way that employees interact with organizational knowledge and enterprise systems. Q Business customers in this region can get answers from enterprise RAG knowledge bases and uploaded files (e.g. pdf’s, images) and run tabular search on small tables. Customers can also get answers from LLM knowledge and generate content using their Q Business assistant. Amazon Q Business connects seamlessly to over 40 popular enterprise systems, including Amazon Simple Storage Service (Amazon S3), Microsoft 365, and Salesforce. It ensures that users access content securely with their existing credentials using single sign-on, according to their permissions, and enterprise-level access controls.
With this regional expansion, Amazon Q is now available in the following regions: US East (N. Virginia), US West (Oregon) and Europe West (Ireland) AWS Regions. To learn more about the Amazon Q Business features available in this region, go to Q Business service regions. For more information, see Amazon Q Business.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7g instances are available in the AWS Israel (Tel Aviv) region. These instances are powered by AWS Graviton3 processors that provide up to 25% better compute performance compared to AWS Graviton2 processors, and built on top of the the AWS Nitro System, a collection of AWS designed innovations that deliver efficient, flexible, and secure cloud services with isolated multi-tenancy, private networking, and fast local storage.

Amazon EC2 Graviton3 instances also use up to 60% less energy to reduce your cloud carbon footprint for the same performance than comparable EC2 instances. For increased scalability, these instances are available in 9 different instance sizes, including bare metal, and offer up to 30 Gbps networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (EBS).

To learn more, see Amazon EC2 M7g. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7g instances are available in the AWS Israel (Tel Aviv) region. These instances are powered by AWS Graviton3 processors that provide up to 25% better compute performance compared to AWS Graviton2 processors, and built on top of the the AWS Nitro System, a collection of AWS designed innovations that deliver efficient, flexible, and secure cloud services with isolated multi-tenancy, private networking, and fast local storage. Amazon EC2 Graviton3 instances also use up to 60% less energy to reduce your cloud carbon footprint for the same performance than comparable EC2 instances. For increased scalability, these instances are available in 9 different instance sizes, including bare metal, and offer up to 30 Gbps networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (EBS). To learn more, see Amazon EC2 M7g. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.    

The post D-ID se asocia con Microsoft para ofrecer avatares impulsados por Al appeared first on Source LATAM.

​The post D-ID se asocia con Microsoft para ofrecer avatares impulsados por Al appeared first on Source LATAM.