Kategorie: Amazon AWS

AWS Continuum for code vulnerabilities is now generally available

Today, AWS announces the general availability of AWS Continuum for code vulnerabilities. Continuum for code vulnerabilities discovers, validates, prioritizes, and remediates code vulnerabilities at machine speed.
Continuum for code vulnerabilities builds a context graph from your AWS environment, including resource topology, identity relationships, and network reachability. It knows which resources are part of your application, and which findings carry real business risk. It ingests your existing vulnerability findings, scans your code, and then confirms which findings are exploitable by testing them in an isolated sandbox. The findings are prioritized by business impact. And finally, it provides remediation patches as pull requests directly to developer workflows with exploitability proof and blast radius assessment attached.
Continuum for code vulnerabilities picks the right AI model for each task. Continuum starts in Learn mode where it proposes the remediation fixes that you approve. When it builds sufficient trust in its precision, you switch to the Enforce mode and let it act autonomous within the guardrails you set. 
AWS Continuum works alongside your existing AWS security services, including Amazon GuardDuty and AWS Security Hub. For more information about the AWS Regions where AWS Continuum is available, see the AWS Region table. To learn more, see the AWS Continuum product page.
Quelle: aws.amazon.com

Amazon Connect Customer now supports customizing the Customer Profiles app on the agent workspace

Amazon Connect Customer Profiles now allows administrators to customize which customer data that agents see in the agent workspace, and how it is displayed, using layouts they build in Profile Explorer. Customer Profiles provides agents with a real-time, unified view of each customer during interactions, combining contact history with customer data from multiple sources. Agents who handle different kinds of requests need different customer data in front of them. Within Profile Explorer, administrators can create and save each layout of Customer Profiles widgets, such as contact history, contact information, and lifetime value calculated attributes, and assign it to agents in place of the legacy Customer Profiles view. For example, an airline baggage claims agent sees contact history, active flights, and baggage ticket information, while a loyalty agent sees loyalty status, preferences, and upcoming flights. Layouts can also include an AI Profile Summary that focuses the agent’s attention on key customer insights by summarizing the customer profile. You can use Profile Explorer in the following AWS Regions: US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), and Europe (London). To learn more about Profile Explorer, see Profile Explorer in the Amazon Connect Customer administrator guide.
Quelle: aws.amazon.com

AWS Lambda MicroVMs is now available in 4 additional AWS regions

AWS Lambda MicroVMs is now available in 4 additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Malaysia), Asia Pacific (Thailand), and Europe (Spain). With this launch, you can use Lambda MicroVMs in a total of 14 AWS Regions.
Lambda MicroVMs is a serverless compute primitive that provides VM-level isolation, near-instant launch and resume speeds, and state preservation for executing user or AI generated code.  Developers can use Lambda MicroVMs to give each user or job their own isolated compute environment without managing virtualization infrastructure or choosing between isolation, speed, and state retention. MicroVMs enable secure, stateful, and responsive code execution for applications including AI coding assistants, interactive development environments, and vulnerability scanners.
With today’s launch, developers can run Lambda MicroVMs closer to their end-users in 4 additional Regions, helping meet latency and data residency requirements. To get started, create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. Give each user or job their own MicroVM with a dedicated HTTPS URL that supports popular connectivity protocols such as HTTP/2, gRPC, and WebSockets. You can use MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or with your preferred Agentic development tools using the Agent Toolkit for AWS.
To learn more, visit the AWS Lambda MicroVMs developer guide. For information on pricing, see Lambda MicroVMs pricing. Visit the AWS Capabilities by Region page for the latest region availability.
Quelle: aws.amazon.com

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen3.8 27B, and Ministral 3 3B, 8B, and 14B models. In addition to deploying these models on SageMaker AI, you can now adapt them to your specific domains and workflows, using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques. You can also adapt these models with either low-rank adaptation (LoRA) or full fine-tuning techniques. This launch adds these models to a wide variety of models available for serverless customization on SageMaker AI, including models from the Nova, Gemma 4, Nemotron 3.5, Qwen, Llama, gpt-oss, and DeepSeek families.
Model customization lets you tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, while also reducing cost and latency. You can use labeled data with SFT to improve accuracy on domain-specific tasks, preference data with DPO to align outputs with your organization’s tone, or reward signals with RFT to improve performance on new tasks. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use.
Serverless model customization on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, use the SageMaker Python SDK or use our Agent skills with your choice of coding assistant. To learn more and see the full supported list of models, see the Amazon SageMaker AI model customization documentation.
Quelle: aws.amazon.com

AWS announces AWS Interconnect – last mile preview with Megaport

AWS launches AWS Interconnect – last mile preview with Megaport, a fully managed connectivity offering that allows customers to connect their branch offices, data centers, and remote locations to AWS with just a few clicks, eliminating the friction and complexity of network setup. As a milestone collaboration between AWS and Megaport, AWS Interconnect – last mile combines AWS cloud innovation with Megaport operating one of the world’s largest Software Defined Networks (SDN), with 1,200+ enabled data centers across 31 countries.
Through the AWS Management Console, customers can now instantly establish private, high-speed connections to AWS by simply choosing their preferred AWS Region, bandwidth speed, Direct Connect Gateway ID and partner subscriber ID. Once initiated, AWS generates an activation key to complete connectivity provisioning with Megaport. The launch simplifies the connectivity experience by pre-provisioning capacity and automating complex network configuration including BGP peering, VLAN configuration, and ASN assignment. At GA, customers will also be able to dynamically scale bandwidth from 1 Gbps to 100 Gbps through the AWS Console. The service is designed for high availability, with zero down-time maintenance, backed by a 99.99% SLA. 
AWS Interconnect – last mile is available in the San Jose (us-west-1) and N. Virginia (us-east-1) metros through Megaport. You can create a preview Interconnect using the AWS Management Console, Command Line Interface (CLI), or API. Partners can also easily adopt via a published open API package on GitHub. For more information, see the AWS Interconnect – last mile documentation and pricing pages.
Quelle: aws.amazon.com

Amazon SageMaker AI announces training session APIs for serverless model customization

Amazon SageMaker AI serverless model customization now supports training session APIs, giving customers full control over their post-training algorithm without provisioning or managing GPU infrastructure. Managed techniques, including SFT, DPO, and other reinforcement learning methods, let customers pick a technique and a model and have SageMaker AI run the training. With training session APIs, teams get to write the training loop, whether that’s reproducing SFT or DPO on their own terms or implementing a custom loss, a new RL reward signal, a data curriculum, or an algorithm straight from the latest research. Operations such as forward_backward(), optim_step(), and sample() become API calls against a live, stateful session, so customers compose whatever training procedure their technique requires while SageMaker AI handles GPU allocation, model and LoRA loading, distributed execution, fault recovery, checkpoint storage, and security. Customers can call these operations directly from their own Python code to drive reinforcement learning, supervised fine-tuning, or custom training objectives against a live session, with checkpointing and resume built in. Billing is per token processed, so local computation like reward scoring isn’t billed as GPU time. SageMaker AI is also releasing the SageMaker training sessions cookbook, a set of ready-to-run SFT and RL recipes customers can use as blueprints for their own training loops.
Training session APIs are generally available today in US East (N. Virginia), US West (Oregon), and Asia Pacific (Mumbai). To get started, install the SageMaker Python SDK and clone the cookbook, or launch a notebook in SageMaker Studio and connect a coding agent. To learn more, visit the documentation and the Amazon SageMaker AI webpage.
Quelle: aws.amazon.com

AWS CloudFormation now simplifies stack update rollback operations

AWS CloudFormation now makes it simpler to complete an update rollback and get back to deploying. An update rollback returns your stack to a known good state. Starting today, if some resources cannot roll back automatically, force rollback enables you to complete the rollback it in a single operation. It works across every CloudFormation workflow, from the AWS CLI, and SDKs to AWS CDK and AI agents.
With force rollback, CloudFormation completes the update rollback and returns your stack to a deployable state in a single operation, instead of requiring you to identify, list, and retry the operation for each affected resource. Any resource that still needs attention, such as one affected a change outside CloudFormation, is recorded for you to review and resolve it before your next deployment.
To get started, run continue update rollback with the force rollback option, then create a drift-aware change set to review any configuration differences before your next deployment. To learn more, see  Continue rolling back an update  in the AWS CloudFormation User Guide.
Force rollback is available in all Regions at no additional cost. Refer to the  AWS Region table  for service availability details.
Quelle: aws.amazon.com

AWS Neuron 2.33 adds native PyTorch support, Neuron Observer, and other new features

AWS Neuron 2.33 is now available, introducing TorchNeuron, the native PyTorch backend for AWS Trainium, in public beta. You can run models written with the standard PyTorch APIs on Amazon EC2 Trn2 and Trn3 instances, in eager mode or with torch.compile. This release also introduces Neuron Observer in public beta, which detects Trainium health issues in under a second and identifies the affected node or device.
The vLLM Neuron plugin upgrades to vLLM 0.29.0, and the vLLM Omni Neuron plugin for Wan2.2 video generation is now part of the Neuron release. Both support native PyTorch compilation through TorchNeuron. A new Neuron Deep Learning AMI and Deep Learning Container for PyTorch training include TorchNeuron, PyTorch 2.14, and TorchTitan. Neuron Explorer opens profiles captured with torch.profiler. From a PyTorch memory snapshot, it now attributes allocations to the Python source lines that made them, for debugging out-of-memory errors in eager mode.
Kernels written with the Neuron Kernel Interface (NKI) can now use the full Python language for metaprogramming, including generators and class inheritance. The NKI Library adds 25 new experimental kernels, including kernels for DeepSeek-V3.2, Qwen3.5, and Kimi Delta Attention.
To get started, see the following resources:

AWS Neuron 2.33.0 Release Notes

AWS TorchNeuron documentation

AWS Neuron Observer documentation

AWS Neuron Kernel Interface documentation

AWS Neuron

Quelle: aws.amazon.com

Amazon Aurora and Amazon RDS now support On-Demand Capacity Reservations

Starting today, you can reserve compute capacity for your Amazon Aurora and Amazon RDS DB instances in one or more Availability Zones for as long as you need it. Reserving capacity helps ensure that DB instance create, restore, scale-compute, and start operations don’t fail because of insufficient capacity.
On-Demand Capacity Reservations are designed for workloads that need assurance that specific DB instance capacity will be available when they need it for business-critical events, such as migrations, product launches, seasonal peaks, high-availability deployments, or disaster recovery. You choose the DB instance class, engine, Availability Zones, and reserved capacity per Availability Zone, and RDS holds that capacity for you. Capacity can be reserved in two or more Availability Zones to support Multi-AZ deployments. To use the reserved capacity, reference the reservation when you create, restore, modify, or start a DB instance.
You can reserve capacity for immediate use with no commitment and cancel at any time, or schedule a reservation to begin 5 to 120 days in the future with a minimum commitment. Reserved capacity is billed at On-Demand rates while the reservation is active, whether or not DB instances within the reservation are running. Discounts from your existing Reserved DB Instances or Database Savings Plans apply automatically.
On-Demand Capacity Reservations are available for Amazon Aurora and Amazon RDS in all AWS commercial Regions. To learn more about On-Demand Capacity Reservations, visit the documentation for Amazon Aurora and Amazon RDS. 
Quelle: aws.amazon.com

Amazon Cognito extends multi-Region replication to AWS GovCloud (US) Regions

Amazon Cognito multi-Region replication (MRR) now supports AWS GovCloud (US) Regions, extending the resilience benefits of MRR to more workloads. MRR lets you synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region you designate, in near real-time. With user data in the standby Region, you can redirect traffic and keep authentication available if there is a disruption in your primary Region. Previously, MRR was available in 16 commercial Regions. You can now also use MRR to replicate between additional AWS Regions, helping you meet resilience, data residency, and compliance requirements in the Regions that best align with your business needs. MRR is available as an add-on for user pools in the Essentials or Plus feature tiers, and requires a multi-Region AWS KMS customer managed key (CMK). For the full list of AWS Regions where Amazon Cognito is available, see AWS Capabilities by Region. To get started, visit the developer guide for instructions and the pricing page for pricing details.
Quelle: aws.amazon.com