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Amazon WorkSpaces introduces support for Red Hat Enterprise Linux

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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.

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 .
 

Amazon SageMaker Canvas announces new capabilities for time series forecasting models

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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.

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 .

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

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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.

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 GuardDuty EC2 Runtime Monitoring now supports Ubuntu and Debian OS

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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 .

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.

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