Q2 silicon wafer shipments increased 7.4% YoY to 3,573 million square inches (MSI) from the 3,327 MSI recorded in Q2 2025 and 9.1% QoQ from the 3,275 MSI recorded oc […]

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Japan is hooking up with companies in Ukraine and the US to stimulate local participation in the  defence drone market which is currently 91% supplied by Chinese firms. Under  a […]

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Today, AWS announces Context Ontology Accelerator, an open source accelerator that helps organizations build an ontology of their business — a machine-readable model of their products, customers, policies, and the rules that define how they operate so AI agents make accurate, consistent, explainable, and auditable decisions. Context Ontology Accelerator connects to your structured and/or unstructured data sources, and uses AI to draft an ontology; your domain experts review, edit, and approve every element of it. The approved ontology is stored in a knowledge graph you own, expressed in open W3C standards so it works with any standards-based tooling, and any agent can consume it through the included Model Context Protocol (MCP) server.

Organizations building AI agents need more trust in their agents’ decisions, with outputs they can explain and audit. The context those agents require — entities, rules, policies, relationships — is scattered across dozens of systems with conflicting schemas and naming conventions. Teams spend months reconciling definitions and encoding them into prompts and point integrations, and because the results are not traceable, teams block agents from moving from proof-of-concept to production. Context Ontology Accelerator reduces what would take months of manual ontology authoring into days — from connecting data to serving governed answers to agents.

Context Ontology Accelerator is available today on GitHub under Apache 2.0. The initial release uses Amazon Neptune as the graph store, Amazon OpenSearch Serverless as the vector store, and foundation models hosted on Amazon Bedrock; deploy it yourself or engage AWS Professional Services. Context Ontology Accelerator’s managed, user-defined ontology capability will become a fully managed feature native to AWS Context. Customers who get started with Context Ontology Accelerator to create ontologies will be able to use and manage them with AWS Context. To learn more, read the documentation .

Today, AWS Transform for full-stack Windows modernization announced general availability of offline source transformation, enabling customers to modernize Microsoft SQL Server databases to Amazon Aurora PostgreSQL without requiring a live database connection. AWS Transform now converts SQL Server storage objects, powered by AWS DMS, and code objects (stored procedures) using an agentic, interactive experience. Enterprises modernizing legacy .NET applications and their dependent SQL Server databases can now start their modernization by directly uploading the Data Design Language (DDL) source files from their databases.

With offline source transformation, customers upload SQL Server data design language (DDL) files, assess database and stored procedure complexity, and generate a customizable transformation plan. AWS Transform converts tables, schemas and converts code objects such as stored procedures and functions, validates functional equivalence, and deploys the converted schema to Aurora PostgreSQL. The same workflow transforms database dependent .NET applications to be PostgreSQL compatible .NET applications with updated connection strings, ADO.NET and Entity Framework data-access calls. To address remaining conversion issues, customers can iterate directly in the web console or hand off to their preferred IDE using the AWS Transform MCP server. A separate synthetic data workflow populates Aurora PostgreSQL with test data for end-to-end application validation.

AWS Transform for full-stack Windows modernization and offline source transformation is available in US East (N. Virginia). To get started, you can go to AWS Transform product page or see AWS Transform for full-stack Windows documentation .

Amazon SageMaker Unified Studio now supports Teradata Vantage as a data source, enabling you to query and analyze your enterprise data warehouse data directly alongside your other data assets. With this new connection, you can combine business-critical data stored in Teradata with data from sources like Amazon Redshift, Amazon S3, and relational databases — all within a single, governed environment.

This integration is particularly valuable when you need to correlate data across analytical and operational workloads. For example, you can join historical enterprise data and business metrics stored in Teradata with real-time datasets to gain deeper insights into customer behavior, financial performance, and operational trends. Data engineers and analysts can build data pipelines that bring together enterprise data warehouse assets with cloud-native datasets, streamlining cross-source workflows without switching between tools. To get started, add a Teradata Vantage connection under your project’s data section. Your Teradata data now appears in the data explorer alongside your other project data. From there, you can query it directly using the query editor, explore it in notebooks, or incorporate it into a visual ETL job, all without leaving the studio.

Support for Teradata Vantage connections is available in all AWS Regions where Amazon SageMaker Unified Studio is available .

To get started, see connecting to a new data source in the Amazon SageMaker Unified Studio User Guide .  

AWS WAF now supports two new partner managed rule groups from Miggo Security, available through AWS Marketplace: Miggo Rules for AWS WAF – High Emerging Application Threats , and Miggo Rules for AWS WAF – AI/ML Application Protection . These rule groups give AWS WAF customers continuously updated protection against vulnerabilities that are being actively exploited, have public proof-of-concept code, or appear in the CISA Known Exploited Vulnerabilities (KEV) catalog, without writing or maintaining custom rules.

The High Emerging Application Threats rule group focuses on vulnerabilities under active exploitation, and the AI/ML Application Protection rule group focuses on generative-AI application stacks such as AI agent frameworks, LLM gateways, and model-serving infrastructure. You can subscribe to either rule group and add it to a web ACL directly in the AWS WAF console through AWS Marketplace, with no additional configuration. Both rule groups support versioning, and pricing is set by Miggo through AWS Marketplace.

To get started, visit the AWS WAF console or find the Miggo rule groups in AWS Marketplace. For more information, see the AWS WAF Developer Guide . For a full list of supported Regions, visit the AWS Regional Services page.

Amazon Elastic Container Registry (Amazon ECR) has increased the maximum image layer size limit to 200 GB, for images pushed via Docker push.

Previously, packaging assets required splitting data across multiple layers or offloading to external storage systems. With this update, customers can store up to 200 GB in a single image layer, eliminating extra complexity for use cases like embedding large language models, bundling genomics datasets, or packaging large binary dependencies directly into your container images. Images pushed using the AWS SDK or CLI; (UploadLayerPartAPI) remain limited to 50 GB.

This feature is available in all AWS Regions and partitions where Amazon ECR is available except the Middle East (Bahrain) and Middle East (UAE) Regions. To learn more, visit the Amazon ECR product page and refer to the Amazon ECR User Guide . For pricing information, see the Amazon ECR pricing page .

Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances.

I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks.

I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page .

Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it.

Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use.

Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK . To learn more, and see the supported list of models, see the Amazon SageMaker AI model customization documentation.

AWS Config now supports 15 additional AWS resource types across key services including Amazon Bedrock,  Amazon OpenSearch Serverless, and Amazon SageMaker. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.

With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators.

You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the resources are available:

Resource Types:

AWS::AppSync::DomainName	AWS::OpenSearchServerless::AccessPolicy
AWS::Bedrock::AutomatedReasoningPolicy	AWS::OpenSearchServerless::LifecyclePolicy
AWS::Bedrock::AutomatedReasoningPolicyVersion	AWS::SageMaker::ImageVersion
AWS::Bedrock::Blueprint	AWS::SageMaker::InferenceComponent
AWS::Bedrock::DataAutomationProject	AWS::SageMaker::PartnerApp
AWS::BedrockAgentCore::ApiKeyCredentialProvider	AWS::SageMaker::Project
AWS::Connect::UserHierarchyGroup	AWS::SageMaker::Space
AWS::Glue::Trigger