Tag: Internet

Today, AWS announces the general availability of Custom Instructions for Q&A topics in Amazon Q in QuickSight. Custom Instructions enables Author Pros to curate Amazon Q's responses to questions by adding domain-specific knowledge that can’t be captured through a topic’s metadata settings, such as synonyms or semantic types. By providing these metadata descriptions or custom instructions, Authors can guide Amazon Q to align its responses with distinct definitions, preferences, and expert knowledge—ensuring more accurate, relevant, and tailored answers that are better suited for their business needs. Custom Instructions are available to Amazon Q in QuickSight Pro Author and Pro Admin users in all supported regions. To learn more, visit Amazon Q in QuickSight and explore the

Today, AWS announces the release of the Model Context Protocol (MCP) server for AWS Price List, now available in the AWS Labs GitHub repository. The MCP server provides AI agents with real-time access to AWS product data, availability information and pricing details, including on-demand, reserved, and savings plans. With this launch, you can leverage AI assistants, including Amazon Q Developer CLI, and Claude Desktop, to retrieve AWS product and pricing information, compare pricing options across regions, and make data-driven decisions about AWS service selections - all through natural language conversations. You can download and integrate the open-source server with your preferred MCP-compatible AI assistant. The

Amazon DocumentDB (with MongoDB compatibility) Global Clusters now supports adding up to 10 secondary Regions, further enhancing scalability and availability for globally distributed applications. Amazon DocumentDB Global Clusters spans multiple AWS Regions, providing disaster recovery from Region-wide outages and enabling fast local reads for globally distributed applications. This launch increases the number of secondary Regions that can be added to a global cluster from the previously supported limit of up to 5 secondary Regions to up to 10 secondary Regions, providing a larger global footprint for operating your applications. See documentation to learn more about global clusters. Amazon DocumentDB (with MongoDB compatibility) is a fully managed, native JSON database that makes it simple and cost-effective to operate critical document workloads at virt

The AWS Price List Query API now supports four new filter types, giving you more precise ways to search product data. With the new filters, you can now search products by exactly matching attributes, using sub-string matches, or creating include/exclude lists for more targeted results. These new filters simplify product discovery, enabling more efficient data retrieval. You can now easily find specific groups of products—such as all m5 EC2 instance types—with a single, streamlined filter instead of creating multiple complex queries. The new filter capabilities are available in all regions where the AWS Price List API is supported. Visit the userguide to learn more.

Starting today, customers can use Amazon Managed Service for Apache Flink in Asia Pacific (Taipei) Region to build real-time stream processing applications. Amazon Managed Service for Apache Flink makes it easier to transform and analyze streaming data in real time with Apache Flink. Apache Flink is an open source framework and engine for processing data streams. Amazon Managed Service for Apache Flink reduces the complexity of building and managing Apache Flink applications and integrates with Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis Data Streams, Amazon OpenSearch Service, Amazon DynamoDB streams, Amazon Simple Storage Service (Amazon S3), custom integrations, and more using built-in connectors. You can learn more about Amazon Managed Service for Apache Flink here. For Amazon Managed Service for Apache Flink region availability, refer to

AWS announces the release of AWS IoT Greengrass v2.15, introducing significant updates to both nucleus and nucleus lite core components. AWS IoT Greengrass is an Internet of Things (IoT) edge runtime and cloud service that helps customers build, deploy, and manage device software at the edge. The latest release, version 2.15, strengthens core capabilities with enhanced secret and log management, advancing both security posture and troubleshooting efficiency for IoT applications running at the edge. The new release also offers updates for AWS IoT Greengrass nucleus lite, a lightweight, open-source edge runtime, specifically designed for resource-constrained devices with minimal footprint (5MB RAM). The new nucleus lite release introduces Docker containers management, expanding capabilities of lightweight IoT deployments and delivering containerized applications support in resource-constrained environments. AWS IoT Greengrass v2.15 is available in all AWS Regions whe

AWS is pleased to announce a significant enhancement to the AWS Specialization Programs Badge, which is available now. This enhancement will enable AWS Specialization Partners to showcase badges that display both their specific specialization and its corresponding category, addressing frequent feedback from our partners and customers. In today's competitive cloud marketplace, AWS Specializations have become instrumental in partner selection. According to recent Canalys research, 87% of customers consider AWS Specializations among their top three selection criteria, with 60% citing it as their primary factor when choosing an AWS Partner. The enhanced badges will be prominently featured in partners' marketing materials, event displays, social media presence, and customer-facing collateral. This update provides several key benefits: Improved visibility of specialized expertise Enhanced differentiation in the marketplace Stron

Amazon SageMaker HyperPod now supports deploying both open-weights foundation models from Amazon SageMaker JumpStart and your own fine-tuned models from Amazon S3 and Amazon FSx directly to Amazon SageMaker HyperPod. This enables you to seamlessly train, fine tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model lifecycle In a few quick steps, you can choose an open-weights foundation model from SageMaker JumpStart and quickly deploy it on your SageMaker HyperPod cluster. SageMaker automatically provisions the infrastructure, deploys the model on your cluster, enables auto-scaling, and configures the SageMaker endpoint. SageMaker scales the compute resources up and down through HyperPod task governance as the traffic on model endpoints changes, and automatically publishes metrics to the HyperPod observability dashboard to provide full visibility into model performance. You can deploy models from Sage

Today, AWS announces remote connection from Visual Studio Code to Amazon SageMaker Studio development environments, enabling AI developers to use Visual Studio Code with SageMaker AI’s scalable compute resources. This new capability enables developers to connect from Visual Studio Code to SageMaker Studio in minutes instead of hours, enabling them to rapidly scale their model development. SageMaker Studio offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (VS Code - Open Source). Starting today, you can use your customized local VS Code setup, including AI-assisted development tools and custom extensions, while accessing SageMaker AI’s compute resources and your data. You can authenticate using the AWS Toolkit extension in VS Code or through SageMaker Studio's web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks.

We are excited to announce the general availability of the Amazon SageMaker HyperPod Command Line Interface (CLI) and Software Development Kit (SDK). These tools make it easier for developers and ML practitioners to build, train, and deploy large-scale AI models on SageMaker HyperPod. SageMaker HyperPod CLI offers a simple, consistent command-line experience for managing HyperPod clusters and quick experimentation. The SDK offers intuitive programmatic access to HyperPod’s distributed training and inference capabilities and enables developers to have granular control over their workload configurations. With these tools, data scientists and ML engineers can easily launch training jobs, deploy scalable inference endpoints, and monitor cluster performance. Using simple commands, customers can access system logs and HyperPod observability dashboards, enabling them to debug issues and accelerate model development. These new developer interfaces enable customers to build and de