Tag: Internet

Amazon SageMaker HyperPod task governance now supports dynamic resource sharing, allowing teams to borrow unallocated compute capacity in HyperPod clusters beyond their guaranteed quotas. Administrators can also configure borrow limits for specific resource types, such as accelerators, vCPU, or memory, to ensure fair distribution across teams. Administrators running shared compute clusters for generative AI workloads often face underutilization challenges. When data scientists do not fully consume their allocated quotas, expensive compute instances remain idle. Idle resource sharing solves this by automatically identifying unallocated cluster capacity and making it available for teams to borrow on a best-effort basis. HyperPod task governance monitors your cluster state and automatically recalculates borrowable resources when instances and compute quota policies change, eliminating manual configuration. Eligible instances that are in a ready and schedulable state, including

Amazon Neptune now supports reading data from Amazon S3 within openCypher queries. Through the new `neptune.read()` procedure, customers now have an additional option of federating with external data stored in S3 versus needing to load data into Neptune. Organizations using Neptune for graph analytics can now dynamically incorporate S3-stored data without the traditional multi-step workflow requirements. Key use cases include real-time graph analytics that combine S3 data with existing graph structures, dynamic node and edge creation from external datasets, and complex graph queries requiring external reference data. The procedure supports comprehensive data types including standard and Neptune-specific formats such as geometry and datetime, while maintaining security through the caller's IAM credentials. Read from S3 is available in all regions where Amazon Neptune Database is currently offered. To l

Amazon Timestream for InfluxDB now supports expanded multi-node cluster configurations for InfluxDB 3 Enterprise edition, enabling you to scale clusters up to 15 nodes for demanding production workloads requiring high read throughput and high availability. With this launch, you can now configure clusters with up to 15 nodes total, with one to four writer/reader nodes for data ingestion and queries, zero to 13 dedicated reader-only nodes for scaling query performance, plus a dedicated compactor node. This enables you to optimize for specific workload patterns. For example, you can create a dedicated reader-only nodes to handle read-heavy workloads such as dashboards, reporting, and analytical queries without impacting write performance. All Multi-node deployments distribute workloads across multiple nodes in different Availability Zones for enhanced fault tolerance and high availability With this release, you can now add and remove nodes from all Enterprise clusters

Today, AWS announces the general availability of AWS Partner Central agents, new AI-powered capabilities designed to accelerate partner co-selling with AWS. Built on Amazon Bedrock AgentCore, these agentic capabilities work alongside partner sales teams to shorten sales cycles and simplify funding access. AWS Partners can engage with these agentic capabilities directly in the console or programmatically through Model Context Protocol (MCP), enabling sales teams to access from within their own customer relationship management (CRM) systems. With AWS Partner Central agents, partner teams get pipeline insights, tailored sales plays, and next-step recommendations on demand, so they know where to focus and what to do next. Partner sales teams can share meeting transcripts, notes, or emails with agents that automatically populate fields and advance deals, so they stay focused on selling, not data entry. Agents recommend funding at the opportunity level, highlight eligibility gaps,

Amazon SimpleDB now supports exporting domain data directly to Amazon S3 buckets in standard JSON format. Exports run in the background with no impact on database performance, making it simple to migrate data to other systems or meet data archival requirements. The export tool offers features including cross-region and cross-account support, multiple encryption options, and flexible S3 bucket configuration. Key use cases include migrating data for long-term archival or compliance purposes. The tool provides three new APIs (StartDomainExport, GetExport, and ListExports) with built-in rate limiting of 5 exports per domain and 25 per account within 24 hours. There is no charge to use this tool. However, standard data transfer charges apply.   The export tool is available in all regions where Amazon SimpleDB is available. You can get started with the export tool by using the AWS API or CLI. For more information, see the 

Amazon Connect now enables agents to forward email contacts to external email addresses and distribution lists directly from the Agent workspace and Contact Center Panel. When an email is forwarded, agents still retain ownership and complete communication trail of the original contact. This makes it easy for your agents to seamlessly loop in back-office teams, subject matter experts, partners, and other stakeholders, while remaining a single consistent point of contact for your customers. Amazon Connect email is available in the US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) regions. To learn more and get started, please refer to the help

Amazon Bedrock AgentCore Runtime now supports the Agent-User Interaction (AG-UI) protocol, enabling developers to deploy AG-UI servers that deliver responsive, real-time agent experiences to user-facing applications. With AG-UI support, AgentCore Runtime handles authentication, session isolation, and scaling for AG-UI workloads, allowing developers to focus on building interactive frontends for their agents. AG-UI is an open, event-based protocol that standardizes how AI agents communicate with user interfaces. It complements the existing Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol support in AgentCore Runtime. Where MCP provides agents with tools and A2A enables agent-to-agent communication, AG-UI brings agents into user-facing applications. Key capabilities include streaming text chunks, reasoning steps, and tool results to frontends as they happen; real-time state synchronization that can update UI elements such as progress bars and dashboards; structur

Amazon CloudWatch Application Signals now offers three new console based capabilities for Service Level Objectives (SLOs): SLO Recommendations, Service-Level SLOs, and SLO Performance Report. CloudWatch Application Signals helps customers monitor and improve application performance on AWS. It automatically collects data from applications running on services like Amazon EC2, Amazon ECS, and Lambda. Previously, customers had to manually set SLO thresholds without data-driven guidance, often leading to misconfigured targets and alert fatigue. They also lacked visibility into overall service health across operations and had no way to track reliability trends over time or generate calendar periods performance reports. These new capabilities address each of those gaps, making it easier to set data-driven reliability targets, monitor overall service health, and identify reliability trends before they become incidents.

AWS announces the AWS Serverless Application Model (SAM) Kiro power, bringing serverless application development expertise to agentic AI development in Kiro. With this power, you can build, deploy, and manage serverless applications with AI agent-assisted development directly in your local environment. SAM is an open-source framework that simplifies building serverless applications on AWS. SAM Kiro power dynamically loads relevant guidance and development expertise the AI agent needs to build serverless applications. This includes initializing SAM projects, building and deploying applications to AWS, and locally testing Lambda functions. The power supports event-driven patterns with Amazon EventBridge, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis, Amazon DynamoDB Streams, and Amazon Simple Queue Service (SQS), while covering security best practices for IAM policies. Built-in guidance enf

Starting today, Amazon EC2 R8a instances are now available in Asia Pacific (Tokyo) Region. These instances, feature 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to R7a instances. R8a instances deliver 45% more memory bandwidth compared to R7a instances, making these instances ideal for latency sensitive workloads. Compared to Amazon EC2 R7a instances, R8a instances provide up to 60% faster performance for GroovyJVM, allowing higher request throughput and better response times for business-critical applications. Built on the AWS Nitro System using sixth generation Nitro Cards, R8a instances are ideal for high performance, memory-intensive workloads, such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics,