6/28/2024, 12:00:00 AM ~ 7/1/2024, 12:00:00 AM (UTC)

Recent Announcements

Amazon GuardDuty EC2 Runtime Monitoring now supports Ubuntu and Debian OS

The Amazon GuardDuty EC2 Runtime Monitoring eBPF security agent now supports Amazon Elastic Compute Cloud (Amazon EC2) workloads that use the Ubuntu (Ubuntu 20.04, Ubuntu 22.04) and Debian (Debian 11 and Debian 12) operating system. If you use GuardDuty EC2 Runtime Monitoring with automated agent management then GuardDuty will automatically upgrade the security agent for your Amazon EC2 workloads. If you are not using automated agent management, you are responsible for upgrading the agent manually. You can view the current agent version running in your Amazon EC2 instances in the EC2 runtime coverage page of the GuardDuty console. If you are not yet using GuardDuty EC2 Runtime Monitoring, you can enable the feature for a 30-day free trial with a few steps.\n GuardDuty Runtime Monitoring helps you identify and respond to potential threats, including instances or self-managed containers in your AWS environment associated with suspicious network activity, such as querying IP addresses associated with cryptocurrency-related activity, or connections to a Tor network as a Tor relay. Threats to compute workloads often involve remote code execution that leads to the download and execution of malware. GuardDuty Runtime Monitoring provides visibility into suspicious commands that involve malicious file downloads and execution across each step, providing earlier discovery of threats during initial compromise—before they become business-impacting events.

EvolutionaryScale’s ESM3, a frontier language model family for biology, now available on AWS

EvolutionaryScale’s ESM3 1.4B open source language model is now generally available on AWS through Amazon SageMaker JumpStart and AWS HealthOmics, with the full family coming soon. Amazon SageMaker JumpStart is a ML hub with foundation models, built-in algorithms, and prebuilt ML solutions that can be deployed with just a few clicks. AWS HealthOmics is a purpose-built service that helps healthcare and life science organizations analyze biological data.\n EvolutionaryScale, a frontier AI research lab and Public Benefit Corporation dedicated to developing AI for biology’s most complex problems, has released the cutting-edge ESM3 family of models. ESM3 is a biological frontier model family capable of generating entirely new proteins that have never existed in nature. ESM3 can generate proteins based on sequence, structure, and/or functional constraints – a novel “programmable biology” approach. Trained on billions of protein sequences spanning 3.8 billion years of evolution, ESM3 is one of the largest and most advanced generative AI models ever applied to biology. EvolutionaryScale’s ESM3 1.4B open source model is available in Amazon SageMaker JumpStart initially in US East (Ohio) and in all available AWS HealthOmics regions, except Asia Pacific (Singapore). To learn more, read the blog and press release. To get started with ESM3, visit SageMaker JumpStart website and AWS HealthOmics GitHub repository.

Amazon EventBridge announces new console dashboard

Amazon EventBridge announces a new console dashboard providing you with a centralized view of your EventBridge resources, metrics, and quotas. The dashboard leverages CloudWatch metrics, allowing you to monitor account level metrics such as PutEvents, Matched Events, and Invocations for Buses, Concurrency and Throttles for Pipes, and Invocations and Errors for ScheduledGroups. Additionally, the dashboard allows you to view your default and applied quotas and navigate to the Service Quotas page to request increases, enabling you to respond quickly to changes in usage.\n The Amazon EventBridge Event Bus is a serverless event router that enables you to create scalable event-driven applications by routing events between your own applications, SaaS applications, and AWS services. EventBridge Pipes provides a consistent, and cost-effective way to create point-to-point integrations between event producers and consumers. The EventBridge Scheduler makes it simple for developers to create, execute, and manage scheduled tasks at scale. The new console dashboard surfaces account level metrics, providing deeper insight into your event-driven applications and allowing you to quickly identify and resolve issues as they arise. You can use the dashboard to answer basic questions such as “How many Buses and Pipes have I configured in my account?”, “What was my PutEvent traffic pattern for the last 3 hours?” or “What is the concurrency of my Pipe?”. You can further analyze and customize these dashboards in CloudWatch.

Amazon EC2 High Memory instances now available in Asia Pacific (Hong Kong) Region

Starting today, Amazon EC2 High Memory instances with 3TiB of memory are now available in Asia Pacific (Hong Kong) region. Customers can start using these new High Memory instances with On Demand and Savings Plan purchase options.\n Amazon EC2 High Memory instances are certified by SAP for running Business Suite on HANA, SAP S/4HANA, Data Mart Solutions on HANA, Business Warehouse on HANA, and SAP BW/4HANA in production environments. For details, see the Certified and Supported SAP HANA Hardware Directory. For information on how to get started with your SAP HANA migration to EC2 High Memory instances, view the Migrating SAP HANA on AWS to an EC2 High Memory Instance documentation. To hear from Steven Jones, GM for SAP on AWS on what this launch means for our SAP customers, you can read his launch blog.

AWS ParallelCluster 3.10 with support for Amazon Linux 2023 and Terraform

AWS ParallelCluster 3.10 is now generally available. Key features of this release include support for Amazon Linux 2023 and Terraform. With Terrafrom support, customers can automate deployment and management of clusters similar to how they use Terraform to automate other parts of their AWS infrastructure. Other important features in this release include:\n

Support for connecting clusters to an external Slurm database daemon (Slurmdbd) to follow best practices of enabling Slurm accounting in a multi-cluster environment.

A new allocation strategy configuration to allocate EC2 Spot instances from the lowest-priced, highest-capacity availability pools to minimize job interruptions and save costs.

For more details on the release, review the AWS ParallelCluster 3.10.0 release notes. AWS ParallelCluster is a fully-supported and maintained open-source cluster management tool that enables R&D customers and their IT administrators to operate high-performance computing (HPC) clusters on AWS. AWS ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific, engineering, and machine-learning (ML/AI) workloads at scale on AWS.

Amazon SageMaker Model Registry now supports cross-account machine learning (ML) model sharing

Today, we’re excited to announce that Amazon SageMaker Model Registry now integrates with AWS Resource Access Manager (AWS RAM), making it easier to securely share and discover machine learning (ML) models across your AWS accounts.\n Data scientists, ML engineers, and governance officers need access to ML models across different AWS accounts such as development, staging and production to make the relevant decisions. With this launch, customers can now seamlessly share and access ML models registered in SageMaker Model Registry between different AWS accounts. Customers can simply go to the AWS RAM console or CLI, specify the Amazon SageMaker Model Registry model that needs to be shared, and grant access to specific AWS accounts or to everyone in the organization. Authorized users can then instantly discover and use those shared models in their own AWS accounts . This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization.

Amazon EventBridge Pipes now supports AWS PrivateLink

Amazon EventBridge Pipes now supports AWS PrivateLink, allowing you to access Pipes from within your Amazon Virtual Private Cloud (VPC) without traversing the public internet. With today’s launch, you can leverage EventBridge Pipes features from a private subnet without the need to deploy an internet gateway, configure firewall rules, or set up proxy servers.\n Amazon EventBridge lets you use events to connect application components, making it easier to build scalable event-driven applications. EventBridge Pipes provides a simple, consistent, and cost-effective way to create point-to-point integrations between event producers and consumers. Pipes enables you to send data from one of 7 different event sources to any of the 20+ targets supported by the EventBridge Event Bus, including HTTPS endpoints through EventBridge API Destinations and event buses themselves. Today’s release of AWS PrivateLink support further reduces the amount of integration code you need to write and infrastructure you need to maintain when building event-driven applications. AWS PrivateLink support for EventBridge Pipes is available in all AWS Regions where EventBridge Pipes is available. To get started, follow the directions provided in the AWS PrivateLink documentation. To learn more about Amazon EventBridge Pipes, visit the EventBridge documentation.

Amazon SageMaker now supports SageMaker Studio Personalization

We are excited to announce that Amazon SageMaker now allows admins to personalize the SageMaker Studio experience for their end-users. Admins can choose to hide applications and ML Tools from SageMaker Studio based on the end user preferences.\n Starting today, admins can use the new personalization capability while setting up domains and user-profiles on SageMaker Console or using APIs, and tailor the SageMaker Studio interface. They can curate experiences by selectively showing or hiding specific ML tools, applications and IDEs for specific personas to align closely with how users interact with the platform. This improves SageMaker Studio usability and provides a more intuitive and user-friendly experience. Data scientists and ML engineers can now easily discover and select ML features required to complete their workflows, leading to a better developer productivity. You can get started by creating or editing a domain, or a user profile in SageMaker Console or by using SageMaker APIs. This feature is available in all Amazon Web Services regions where SageMaker Studio is currently available. To learn more, visit documentation.

Amazon Q in Connect now recommends step-by-step guides

Amazon Q in Connect, a generative-AI powered assistant for contact center agents, now recommends step-by-step guides in real-time, which agents use to quickly take action to resolve customers’ issues. Amazon Q in Connect uses the real-time conversation with a customer to detect the customer’s intent and provides a guided workflow that leads an agent through each step needed to solve the issue, reducing handle time and increasing first contact resolution rates and customer satisfaction. For example, when a customer contacts a financial services company, Amazon Q in Connect analyzes the conversation and detects the customer wants to open a retirement plan. Amazon Q in Connect then provides the agent with a guide that enables the agent to collect the necessary information, deliver the required disclosures, and automatically open the account. To learn more about Amazon Q in Connect, please visit the website or see the help documentation.

Amazon WorkSpaces introduces support for Red Hat Enterprise Linux

AWS today announced support for Red Hat Enterprise Linux on Amazon WorkSpaces Personal. This operating system includes built-in security features that help organizations to run virtual desktops securely, while increasing agility and reducing cost. With this launch, WorkSpaces Personal customers have the flexibility to choose from a wider range of operating systems including Red Hat Enterprise Linux, Ubuntu Desktop, Amazon Linux 2, and Microsoft Windows.\n With Red Hat Enterprise Linux on WorkSpaces Personal, IT organizations can enable developers to work in an environment that is consistent with their production environment, and provide power users like engineers and data scientists with on-demand access to Red Hat Enterprise Linux environments whenever necessary—quickly spinning up and tearing down instances and managing the entire fleet through the AWS Console, without the burden of capacity planning or license management. WorkSpaces Personal offers a wide range of high-performance, license-included, fully-managed virtual desktop bundles—enabling organizations to only pay for the resources they use. Red Hat Enterprise Linux on WorkSpaces Personal is available in all AWS Regions where WorkSpaces Personal is available, except for AWS China Regions. Depending on the WorkSpaces Personal running mode, you will be charged hourly or monthly for your virtual desktops. For more details on pricing, refer to Amazon WorkSpaces Pricing. To get started with Red Hat Enterprise Linux on WorkSpace Personal log on to AWS Management Console, navigate to the WorkSpaces service and follow the Amazon WorkSpaces administration guide.

Announcing Data Quality Definition Language (DQDL) enhancements for AWS Glue Data Quality

Customers use AWS Glue Data Quality, a feature of AWS Glue, to measure and monitor quality of their data. They author data quality rules using DQDL to ensure their data is accurate . Customers need the ability to author rules for complex business scenarios that include filter conditions, exclusion conditions, validations for empty values, and composite rules . Previously customers authored SQL to perform these data quality validations in the CustomSQL rule type. Today, AWS Glue announces new set of new enhancements to DQDL that allows data engineers easily author complex data quality rules using native rule types. DQDL now supports\n

NOT operator allowing customers to exclude certain values in their rule.

New keywords such as NULL, EMPTY, and WHITESPACES_ONLY to author rules that capture missing values without complex regular expressions.

Composite rules for customers to author sophisticated business rules. They can now specify options to manage the evaluation order of these rules.

WHERE clause in DQDL to filter data before applying rules.

Refer to DQDL guide for more information. AWS Glue Data Quality is available in all commercial regions where AWS Glue is available. To learn more, visit the AWS Glue product page and our documentation.

Amazon SageMaker Canvas announces new capabilities for time series forecasting models

Amazon SageMaker Canvas announces new capabilities to build, evaluate, and deploy time series forecasting models, providing greater flexibility and ease of use to build forecasting applications. Amazon SageMaker Canvas is a no-code workspace that empowers analysts and citizen data scientists to build, customize, and deploy machine learning (ML) models to generate accurate predictions.\n To build time series forecasting models, SageMaker Canvas uses up to six built-in algorithms to create a custom ensemble of models for each item in your time series, resulting in highly accurate models. Starting today, SageMaker Canvas provides visibility into these algorithms and the flexibility to choose any combination of these algorithms to build your time series forecasting model. Once the model is built, SageMaker Canvas provides a leaderboard with a ranked list of model candidates including a recommendation for the best model based on your dataset and the problem to be solved. You can review key performance metrics for each model on the leaderboard and select a model of your choice. The selected model can then be deployed into production on an Amazon SageMaker real-time inference endpoint for use in applications outside SageMaker Canvas. To access the algorithm selection, model leaderboard, and direct deployment to real-time endpoint capabilities for time series forecasting, log out and log back in to SageMaker Canvas. The new capabilities are now available in all AWS regions where SageMaker Canvas is supported. To learn more, refer to the SageMaker Canvas product documentation.

AWS Elemental MediaTailor now supports CMAF for dynamic ad transcoding

AWS Elemental MediaTailor now supports Common Media Application Format (CMAF) segments for personalized HLS streams and will automatically transcode ad creatives to match.\n Previously, if you wanted to serve CMAF ad segments, you had to create a custom transcode profile configuration. MediaTailor will now detect when the content source is CMAF or ISOBMFF in a DASH or HLS stream and dynamically transcode the ad creatives to match the program source with no additional user configuration required. There is no additional cost for CMAF ad transcoding. AWS Elemental MediaTailor is a channel assembly and personalized ad-insertion service for video providers to create linear over-the-top (OTT) channels using existing video content. The service then lets you monetize those channels—or other live streams—with personalized advertising across the broadest range of devices with a seamless viewer experience. MediaTailor functions independently or as part of AWS Media Services, a family of services that form the foundation of cloud-based workflows. Visit the AWS region table for a full list of AWS Regions where AWS Elemental MediaTailor is available. To learn more about MediaTailor, please visit the product page.

Amazon CodeCatalyst now allows conversion of source repositories to custom blueprints

Today, AWS announces a new capability that enables customers to convert an existing source repository into a custom blueprint in Amazon CodeCatalyst. Custom blueprints give teams the ability to define and propagate best practices for application code, workflows, and infrastructure. However, many customers have already defined these best practices in one or more existing source repositories. Previously, they needed to create a custom blueprint, and manually copy relevant artifacts from their existing source repository into the blueprint project. Now customers have a one-click option to convert an existing repository to a custom blueprint. For more information, see Converting source repositories to custom blueprints.\n Teams can use these custom blueprints to create CodeCatalyst projects or add functionality to existing projects. As the blueprint gets updated with the latest best practices or new options, it can regenerate the relevant parts of your codebase in projects containing that blueprint. For more information, see the CodeCatalyst Blueprints webpage and blueprints documentation.

AWS CodeBuild build timeout limit increased to 36 hours

AWS CodeBuild now enables customers to increase their build timeout up to 36 hours compared to the prior limit of 8 hours. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment.\n This setting represents the maximum amount of time before CodeBuild stops a build request if it is not complete. With this launch, customers with workloads requiring longer timeouts, such as large automated test suites or embedded machine builds, can leverage CodeBuild. The increased timeout limit is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page. To learn more about CodeBuild configurations, please visit our documentation. To learn more about how to get started with CodeBuild, visit the AWS CodeBuild product page.

AWS Blogs

AWS Japan Blog (Japanese)

Containers

AWS Database Blog

AWS for Industries

AWS for M&E Blog

Open Source Project

AWS CLI

AWS CDK

Amplify for Flutter

Amazon EKS Anywhere