AWS Machine Learning
Amazon Web Services' official AI/ML blog covering Bedrock, SageMaker, AgentCore, and Nova model updates.
AWS ML's blog has become an agentic-infrastructure showcase, not a model gallery.
◆Recent moves
- 8h ago
Scale Robot Reinforcement Learning with NVIDIA Isaac Lab on Amazon SageMaker AI
A walkthrough for training Unitree H1 humanoid policies on SageMaker HyperPod and Training Jobs. It pushes SageMaker into robotics reinforcement learning, a workload class AWS rarely highlights, fitting the broader move toward running heavier agent and ML training on managed compute.
View source ↗ - 12h ago
Hands-free first notice of loss: Using Strands Agents and Amazon Bedrock AgentCore Browser Tool for intelligent claims intake
Demonstrates the AgentCore Browser Tool driving live insurance-portal interaction for claims intake, paired with Strands Agents for reasoning. Another concrete agent-does-the-screen-work pattern in the agentic push.
View source ↗ - 12h ago
Build an agentic incident triage assistant with Amazon Quick and New Relic
Amazon Quick orchestrates a New Relic MCP server and Asana to assemble a root-cause brief from one prompt. Shows AWS leaning on MCP as the connective tissue between its agents and external tools.
View source ↗ - 1d ago
Unlocking AI flexibility in Europe: A guide to cross-region inference for EU data processing and model access
Cross-Region Inference on Bedrock routes requests across EU regions to balance capacity against data-residency rules. A compliance-shaped capability that matters more as agent workloads scale in regulated markets.
View source ↗ - 1d ago
It’s safe to close your laptop now: Hosting coding agents on Amazon Bedrock AgentCore
⚡ SPARKPositions Bedrock AgentCore Runtime as the place to host coding agents (Claude Code, Codex, Kiro, Cursor), each in its own isolated microVM with a persistent workspace. The clearest statement yet that AWS wants to run agents, not just serve the models behind them.
View source ↗ - 1d ago
Better decisions at scale: How mathematical optimization delivers where intuition fails
An explainer on where mathematical optimization fits in the AI landscape, with Innovation Center case studies. Thought-leadership rather than a capability change, off the main agentic thread.
View source ↗