Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M8g instances are available in AWS Asia Pacific (Tokyo, Sydney) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon M7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. M8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

To learn more, see Amazon EC2 M8g Instances. 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) M8g instances are available in AWS Asia Pacific (Tokyo, Sydney) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon M7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. M8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 M8g Instances. 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.  

AWS Batch now supports Amazon Elastic Container Service (ECS) Exec and AWS FireLens log router for AWS Batch on Amazon ECS and AWS Fargate. With ECS Exec you can track the progress of your application and troubleshoot issue by by running interactive commands against the containers in your AWS Batch job. AWS FireLens allows you to stream logs of your AWS Batch jobs to your chosen destinations including Amazon CloudWatch, Amazon S3, Amazon OpenSearch Service, Amazon Redshift, partner services such as Splunk and more.

You can configure ECS Exec and AWS FireLens while registering a new AWS Batch job definition or making a revision to an existing job definition. For more information, see Register Job Definition page in the AWS Batch API reference and Amazon ECS Developer Guide for ECS Exec and AWS FireLense.

AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. ECS Exec and AWS FireLens are supported in any AWS Region where AWS Batch is available.

​AWS Batch now supports Amazon Elastic Container Service (ECS) Exec and AWS FireLens log router for AWS Batch on Amazon ECS and AWS Fargate. With ECS Exec you can track the progress of your application and troubleshoot issue by by running interactive commands against the containers in your AWS Batch job. AWS FireLens allows you to stream logs of your AWS Batch jobs to your chosen destinations including Amazon CloudWatch, Amazon S3, Amazon OpenSearch Service, Amazon Redshift, partner services such as Splunk and more. You can configure ECS Exec and AWS FireLens while registering a new AWS Batch job definition or making a revision to an existing job definition. For more information, see Register Job Definition page in the AWS Batch API reference and Amazon ECS Developer Guide for ECS Exec and AWS FireLense. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. ECS Exec and AWS FireLens are supported in any AWS Region where AWS Batch is available.  

The Amazon EventBridge connector for Apache Kafka Connect is now generally available. This open-source connector streamlines event integration of Kafka environments with dozens of AWS services and partner integrations without writing custom integration code or running multiple connectors for each target. The connector includes built-in support for Kafka schema registries, offloading large event payloads to S3, and IAM role-based authentication, and is available under the Apache 2.0 license in the AWS GitHub organization.

Amazon EventBridge is a serverless event router that enables you to create highly scalable event-driven applications by routing events between your own applications, third-party SaaS applications, and other AWS services. With the EventBridge Connector for Apache Kafka Connect, customers can leverage advanced features such as dynamic event filtering, transformation, and scalable routing through a unified connector in Kafka environments. The connector simplifies event routing from Kafka to AWS targets, custom applications and third-party SaaS services. Organizations can deploy the connector on any Apache Kafka Connect installation, including Amazon Managed Streaming for Kafka (MSK) Connect.

This feature is available in all AWS Regions, including AWS GovCloud (US). To get started, download the latest release from GitHub, configure it in your Kafka Connect environment, and refer to our developer documentation for detailed implementation guidance. Amazon MSK users can find specific instructions in the MSK Connect developer guide.
 

​The Amazon EventBridge connector for Apache Kafka Connect is now generally available. This open-source connector streamlines event integration of Kafka environments with dozens of AWS services and partner integrations without writing custom integration code or running multiple connectors for each target. The connector includes built-in support for Kafka schema registries, offloading large event payloads to S3, and IAM role-based authentication, and is available under the Apache 2.0 license in the AWS GitHub organization. Amazon EventBridge is a serverless event router that enables you to create highly scalable event-driven applications by routing events between your own applications, third-party SaaS applications, and other AWS services. With the EventBridge Connector for Apache Kafka Connect, customers can leverage advanced features such as dynamic event filtering, transformation, and scalable routing through a unified connector in Kafka environments. The connector simplifies event routing from Kafka to AWS targets, custom applications and third-party SaaS services. Organizations can deploy the connector on any Apache Kafka Connect installation, including Amazon Managed Streaming for Kafka (MSK) Connect. This feature is available in all AWS Regions, including AWS GovCloud (US). To get started, download the latest release from GitHub, configure it in your Kafka Connect environment, and refer to our developer documentation for detailed implementation guidance. Amazon MSK users can find specific instructions in the MSK Connect developer guide.    

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Amazon Web Services (AWS) announces the availability of Amazon EC2 I7ie instances in the AWS Europe (Ireland) region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances.

I7ie instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads.

I7ie instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 100 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications.

To learn more, visit the I7ie instances page.

​Amazon Web Services (AWS) announces the availability of Amazon EC2 I7ie instances in the AWS Europe (Ireland) region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads. I7ie instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 100 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications. To learn more, visit the I7ie instances page.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7i-flex instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in AWS Asia Pacific (Melbourne) Region. These custom processors, available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers.

M7i-flex instances are the easiest way for you to get price-performance benefits for a majority of general-purpose workloads. They deliver up to 19% better price-performance compared to M6i. M7i-flex instances offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources such as web and application servers, virtual-desktops, batch-processing, and microservices. In addition, these instances support the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. For workloads that need larger instance sizes (up to 192 vCPUs and 768 GiB memory) or continuous high CPU usage, you can leverage M7i instances.

To learn more, visit Amazon EC2 M7i-flex instance page.

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7i-flex instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in AWS Asia Pacific (Melbourne) Region. These custom processors, available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. M7i-flex instances are the easiest way for you to get price-performance benefits for a majority of general-purpose workloads. They deliver up to 19% better price-performance compared to M6i. M7i-flex instances offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources such as web and application servers, virtual-desktops, batch-processing, and microservices. In addition, these instances support the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. For workloads that need larger instance sizes (up to 192 vCPUs and 768 GiB memory) or continuous high CPU usage, you can leverage M7i instances. To learn more, visit Amazon EC2 M7i-flex instance page.  

AWS Lambda@Edge now supports AWS Lambda’s advanced logging controls to improve how function logs are captured, processed, and consumed at the edge. This enhancement provides you with more control over your logging data, making it easier to monitor application behavior and quickly resolve issues.

The new advanced logging controls for Lambda@Edge give you three flexible ways to manage and analyze your logs. New JSON structured logs make it easier to search, filter, and analyze large volumes of log entries without using custom logging libraries. Log level granularity controls can switch log levels instantly, allowing you to filter for specific types of logs like errors or debug information when investigating issues. Custom CloudWatch log group selection lets you choose which Amazon CloudWatch log group Lambda@Edge sends logs to, making it easier to aggregate and manage logs at scale.

To get started, you can specify advanced logging controls for your Lambda functions using Lambda APIs, Lambda console, AWS CLI, AWS Serverless Application Model (SAM), and AWS CloudFormation. To learn more, visit the Lambda Developer Guide, and the CloudFront Developer Guide.
 

​AWS Lambda@Edge now supports AWS Lambda’s advanced logging controls to improve how function logs are captured, processed, and consumed at the edge. This enhancement provides you with more control over your logging data, making it easier to monitor application behavior and quickly resolve issues. The new advanced logging controls for Lambda@Edge give you three flexible ways to manage and analyze your logs. New JSON structured logs make it easier to search, filter, and analyze large volumes of log entries without using custom logging libraries. Log level granularity controls can switch log levels instantly, allowing you to filter for specific types of logs like errors or debug information when investigating issues. Custom CloudWatch log group selection lets you choose which Amazon CloudWatch log group Lambda@Edge sends logs to, making it easier to aggregate and manage logs at scale. To get started, you can specify advanced logging controls for your Lambda functions using Lambda APIs, Lambda console, AWS CLI, AWS Serverless Application Model (SAM), and AWS CloudFormation. To learn more, visit the Lambda Developer Guide, and the CloudFront Developer Guide.    

Today, Amazon Q Business is launching a feature to reduce hallucinations in chat responses. Hallucinations are confident responses made by generative AI applications that are not justified by its underlying data. The new feature enables customers to mitigate hallucinations in real-time during chat conversations.

Large Language Models (LLMs) underlying generative AI applications have reduced the extent of hallucination in their responses, but it is possible that these models could hallucinate. Hallucination mitigation is therefore needed to generate reliable and trustworthy responses. The Q Business hallucination mitigation feature helps ensure more accurate retrieval augmented generation (RAG) responses from data connected to the application. This data could either come from connected data sources, or from files uploaded during chat. During chat, Q Business evaluates a response for hallucinations. If a hallucination is detected with high confidence, it corrects the inconsistencies in its response real-time during chat and generates a new, edited message.

The feature for Amazon Q Business is available in all regions where Q Business is available. Customers can opt into using this feature by enabling it through API or through the Amazon Q console. For more details, refer to the documentation. For more information about Amazon Q Business and its features, please visit the Amazon Q product page.
 

​Today, Amazon Q Business is launching a feature to reduce hallucinations in chat responses. Hallucinations are confident responses made by generative AI applications that are not justified by its underlying data. The new feature enables customers to mitigate hallucinations in real-time during chat conversations. Large Language Models (LLMs) underlying generative AI applications have reduced the extent of hallucination in their responses, but it is possible that these models could hallucinate. Hallucination mitigation is therefore needed to generate reliable and trustworthy responses. The Q Business hallucination mitigation feature helps ensure more accurate retrieval augmented generation (RAG) responses from data connected to the application. This data could either come from connected data sources, or from files uploaded during chat. During chat, Q Business evaluates a response for hallucinations. If a hallucination is detected with high confidence, it corrects the inconsistencies in its response real-time during chat and generates a new, edited message. The feature for Amazon Q Business is available in all regions where Q Business is available. Customers can opt into using this feature by enabling it through API or through the Amazon Q console. For more details, refer to the documentation. For more information about Amazon Q Business and its features, please visit the Amazon Q product page.    

Amazon Q Developer in the AWS Management Console and Amazon Q Developer in the IDE is now GA in the Europe (Frankfurt) Region.

Pro tier customers can now use and configure Amazon Q Developer in the AWS Management Console and Amazon Q Developer in the IDE to store data in the Europe (Frankfurt) Region and perform inference in European Union (EU) Regions giving them more choice over where their data resides and transits. Amazon Q Developer Administrators can configure their user settings so that data is stored in Europe (Frankfurt) Region and inference is performed in EU geographies using cross-region inference (CRIS) to reduce latency and optimize availability. If you are requesting to contact AWS Support your data will be processed in the US East (N. Virginia) region.

Amazon Q Developer in is generally available, and you can use it in the following AWS Regions: US East (N. Virginia), and Europe (Frankfurt).

​Amazon Q Developer in the AWS Management Console and Amazon Q Developer in the IDE is now GA in the Europe (Frankfurt) Region. Pro tier customers can now use and configure Amazon Q Developer in the AWS Management Console and Amazon Q Developer in the IDE to store data in the Europe (Frankfurt) Region and perform inference in European Union (EU) Regions giving them more choice over where their data resides and transits. Amazon Q Developer Administrators can configure their user settings so that data is stored in Europe (Frankfurt) Region and inference is performed in EU geographies using cross-region inference (CRIS) to reduce latency and optimize availability. If you are requesting to contact AWS Support your data will be processed in the US East (N. Virginia) region. Amazon Q Developer in is generally available, and you can use it in the following AWS Regions: US East (N. Virginia), and Europe (Frankfurt).  

The post Desbloquear el futuro de la fabricación con hilos digitales impulsados por IA appeared first on Source LATAM.

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