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Today, we’re announcing support for Fundamental’s NEXUS model on Amazon SageMaker AI. With this launch, you can deploy a foundation model (FM) purpose-built for tabular data prediction. This model helps your enterprise generate accurate, deterministic predictions from structured data in days instead of months. In this post, we show you how to get started with […]

Deep Learning AMI and AWS Deep Learning Containers are now enabled with support for SOCI snapshotter and index. Seekable OCI (SOCI) is a technology that enables efficient container image management through selective file downloading. It uses a layer-based indexing system to map file locations within container images, allowing containers to start with only the necessary

AI agents can autonomously handle complex, multi-step tasks, but their effectiveness depends on calling the right tools to retrieve information or take action. When an agent picks the wrong tool, formats parameters incorrectly, or breaks a workflow chain, task completion times grow, error rates rise, support costs increase, and user experiences degrade. As more organizations

Uncover second-hand scores with AI tools in Google Search and Shopping. ​ 

Large language models (LLMs) deliver strong results on general tasks, but they often struggle with specialized work that requires understanding proprietary data, internal processes, or domain-specific terminology. Amazon Nova Forge addresses this by enabling you to build your own frontier models using Amazon Nova. You can start development from early model checkpoints, blend proprietary data

Traditional computer vision solutions can require significant upfront investment. Setting up data pipelines, model training infrastructure, compute resources, and a dedicated data science team is often prohibitive for small companies or teams. Amazon Nova 2 Lite, available through Amazon Bedrock, provides an appealing alternative solution. This multimodal foundation model detects objects through natural language prompts

Code review was always manual and ineffective because of the inherent disconnect between code and product. Developers could review whether code compiled and worked, but not whether it fulfilled all functional and design requirements. In the past, QA teams spent hours manually clicking through preview environments to ensure features behaved as expected, and even more

In modern development workflows, developers increasingly rely on agentic coding assistants such as Kiro Integrated Development Environment (IDE) to interact with remote tools and services. However, organizations require robust authentication mechanisms to provide secure, identity-verified access between these agentic coding assistants and enterprise Model Context Protocol (MCP) servers. Amazon Bedrock AgentCore is a fully managed

AI agents are only as powerful as the tools they can access. Whether retrieving customer data from a CRM, posting updates to Slack, or querying a GitHub repository, agents need to call external APIs, and that means securely passing credentials at runtime. Getting that right, without hardcoding secrets in code or exposing them in agent

Rare cancer research generates heterogeneous data across genomic sequencing pipelines, clinical trial registries, biomarker repositories, and peer-reviewed literature. Integrating these sources for a single investigation typically requires custom ETL pipelines, manual schema reconciliation, and iterative querying across disconnected systems—a process that can take weeks before any analysis begins. Amazon Quick Research addresses this integration challenge