Tag: SysAdmin

Amazon CloudFront now supports Online Certificate Status Protocol (OCSP) revocation checking for viewer mTLS, enabling you to validate client certificate revocation status in real time during connection establishment. This enables customers using mutual TLS (mTLS) on CloudFront  to verify that client certificates haven't been revoked before accepting connections—a common requirement for regulated industries and zero-trust architectures. Previously, customers implemented certificate revocation using CloudFront Functions and KeyValueStore, maintaining static revocation lists that were only as current as the last manual update. With OCSP, CloudFront queries the responder URL embedded in the client certificate at connection time, validating revocation status directly with the issuing Certificate Authority. CloudFront caches OCSP responses for up to 30 minutes to minimize latency impact on subsequent connections. The OCSP result is exposed in the connection function,

Amazon CloudFront now supports passthrough mode for viewer mutual TLS (mTLS) authentication, enabling customers to forward client certificates to their origin for validation without requiring CloudFront to perform certificate verification. Passthrough mode allows customers with existing mTLS implementations at their origins to use CloudFront without requiring to implement their validation logic at the edge. CloudFront viewer mTLS already supports required mode and optional mode, which offload client certificate authentication to CloudFront using trust stores. Passthrough mode is designed for customers to maintain their existing mTLS validation infrastructure at their origin without requiring any trust store configuration on CloudFront. In passthrough mode, CloudFront forwards every request to the origin along with the client's full certificate chain. Caching is not performed, ensuring each request is authenticated end-to-end by your origin. Connection functions which al

Customers spend days to weeks optimizing prompts and evaluating responses when they want to migrate to a new model or just get better performance out of their current model. They struggle with changing their prompts quickly and then testing them to prevent regressions and improve on underperforming tasks. These situations call for the same tool – a prompt optimizer with built-in evaluations.  Today, Amazon Bedrock introduces Advanced Prompt Optimization, a new tool that allows customers to optimize their prompts for any model on Bedrock, while comparing their original prompts to their optimized prompts across up to 5 models simultaneously. Customers can use this if they are migrating to a new model or just want to get better performance on their current model. If they’re changing models, they can select their current model as a baseline and up to 4 other models. If they aren’

Amazon Web Services announces general availability of Amazon EC2 M3 Ultra Mac instances, powered by the latest Mac Studio hardware. Amazon EC2 M3 Ultra Mac instances are the next-generation EC2 Mac instances, that enable Apple developers to migrate their most demanding build and test workloads onto AWS. These instances are ideal for building and testing applications for Apple platforms such as iOS, macOS, iPadOS, tvOS, watchOS, visionOS, and Safari.    M3 Ultra Mac instances are powered by the AWS Nitro System, providing up to 10 Gbps network bandwidth and 8 Gbps of Amazon Elastic Block Store (Amazon EBS) storage bandwidth. These instances are built on Apple M3 Ultra Mac Studio computers featuring a 28-core CPU, 60-core GPU, 32-core Neural Engine, and 256GB of unified memory. Compared to EC2 M4 Max Mac instances, M3 Ultra Mac instances provide 2x the unified memory, 1.75x the CPU cores, 1.5x the GPU cores, and 2x the Neural Engine cores, gi

Amazon SageMaker AI now supports serverless model customization for Qwen3.6 27B parameter model using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Qwen3.6 is a popular open-weight model family from Alibaba Cloud. This launch is an addition to our support for fine-tuning Qwen3.5 and other popular models. Before this launch, you could deploy Qwen3.6 base model on SageMaker AI and now, you can also adapt it to your specific domains and workflows. Model customization enables you to tailor foundation models with your proprietary data so they more accurately reflect your domain knowledge, terminology, and quality standards. Rather than building models from scratch, fine-tuning lets you start from a capable base model and specialize it for your use cases, whether that's improving accuracy on domain-specific tasks, aligning outputs with your organization's tone, or improving performance on new tasks using your labeled data. With serverless customization, SageMa

AWS Transform brings assessment, migration, and modernization into a single AI-powered experience that guides enterprises through their full transformation journey. Today, AWS announces support for customer-owned Amazon S3 buckets, giving customers full control over where their transformation artifacts are stored and how they are secured. With this launch, you can configure your own S3 bucket, optionally encrypt artifacts with your own AWS KMS key, and manage access policies through your own AWS account. Migration practitioners can upload files directly to their bucket for immediate use by transformation agents and centralize artifact storage across multiple AWS accounts. This is designed to help enterprises in regulated industries meet data sovereignty and compliance requirements without changing how they use AWS Transform. This capability is available in all AWS Regions where AWS Transform is offered. To learn more, see the

Today, as part of the AWS Transform composability initiative, AWS announces the general availability of the agent builder toolkit Kiro power for AWS Transform. With the agent builder toolkit, AWS Partners and customers can build agents tailored to their specific modernization needs and ensure it works seamlessly within AWS Transform. This capability enables Migration and Modernization Competency Partners, ISVs, or customers to create differentiated transformation solutions by integrating their specialized agents, tools, knowledge bases, and workflows with AWS Transform's agentic AI capabilities. The agent builder toolkit provides the end-to-end lifecycle for transformation agents: build agents using the Kiro power; share them with teams or across partner networks, and register them with AWS Transform for discovery. The agent builder toolkit for AWS Transform is available in the Kiro power marketplace. To learn

Today, AWS announces that the AWS Transform agents — built on decades of AWS migration and modernization experience — are now accessible through a Kiro power, agent plugins, and via the AWS Transform MCP server. Developers can now consume all of AWS Transform's capabilities directly from their preferred development environment, whether working interactively in an agentic IDE, managing jobs through the web console, or integrating programmatically via MCP. This launch gives builders flexibility to choose the surface that fits their workflow while gaining the depth of transformation expertise behind the AWS Transform agents for Windows, VMware, mainframe and more. A developer can start a transformation in their agentic IDE, monitor progress and collaborate in the web console, then see results back in their IDE — all against the same underlying job with consistent state. Additionally, AWS Transform now supports IAM role authentication. Customers who start using AWS Transfo

Today, AWS announced the availability of GLM-5.1-FP8 and Phi-4-mini-instruct in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These models from Z.ai and Microsoft bring advanced agentic capabilities and efficient inference to enterprise AI workloads on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: GLM-5.1-FP8 excels at agentic software engineering with sustained multi-round optimization, handling repository-level code generation, terminal tasks, and complex debugging workflows that improve with extended reasoning. It is ideal for automated code review pipelines, AI-powered development environments, and long-horizon problem-solving where the model iterates over hundreds of rounds to refine solutions. Phi-4-mini-instruct excels at strong reasoning, math, and logic in memory-constrained and latency-bound environments, supporting 24 la

Today, AWS announced the availability of Qwen3-TTS-12Hz-1.7B-CustomVoice, Qwen3-TTS-12Hz-1.7B-Base, and Qwen3-ASR-1.7B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models from Qwen bring advanced speech synthesis and recognition capabilities across 10+ languages, enabling customers to build intelligent voice-powered applications on AWS infrastructure. These models address different enterprise speech and audio challenges with specialized capabilities: Qwen3-TTS-12Hz-1.7B-CustomVoice excels at multilingual text-to-speech with customizable voice styles, supporting 10 languages with instruction-driven control over timbre, emotion, and prosody. It is ideal for building real-time interactive voice applications, customer-facing virtual assistants, and content creation workflows that require natural, expressive speech output. Qwen3-TTS-12Hz-1.7B-Base excels at multilingual text-to-sp