AWS extends MLflow and SageMaker Model Registry sync to cross-account governance
AWS blog post describes extending managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern centralizing governance with AWS RAM, and a hybrid pattern keeping development accounts isolated. This follows automatic model registration, addressing model governance across accounts.
Sources and evidence
Summary last validated Sep 8, 2026
Reader actions
Report an issue
Use this for an incorrect summary, wrong source, duplicate story, or wrong category. Submissions are private and do not change the story automatically.
Related coverage
- models
AWS Adds Bedrock Models, Faster AgentCore Agents, Knowledge Base Sync
AWS's September 2026 recap covers Amazon Bedrock, Bedrock AgentCore, and Strands updates: broader model choice, faster serverless agents with built-in evaluation, and automated knowledge base syncing via native enterprise connectors. The post aggregates the month's releases for AI builders across AWS's model and agent tooling.
- agents
Postman runs Agent Mode for 40 million developers on Amazon Bedrock
Postman and AWS detailed the architecture behind Postman's Agent Mode, which serves 40 million developers on Amazon Bedrock. The post covers controlling tool sprawl, exposing schema-based reads, and treating context as the real bottleneck when running an AI agent at scale rather than in a demo.
- infrastructure
Ai2 replaces priority GPU scheduler with budget-based fair-share system
Ai2's AI Infrastructure team replaced its priority-based GPU scheduler with a system using GPU time budgets, hierarchical fair-share allocation, and time-slicing contracts. The institute manages thousands of NVIDIA H100, B200, and B300 GPUs across 88- to 1024-GPU clusters for about 150 researchers, with demand running 2-3x available capacity. The change moved GPU allocation debates into a transparent administrative budgeting process.
- infrastructure
Ai2 details GPU scheduler using time budgets and fair-share allocation
Ai2 published a blog post explaining its new GPU scheduler for its clusters. The scheduler combines time budgets, fair-share allocation, and time-slicing to prioritize high-impact research, shorten queue waits, and keep GPUs busy.
- agents
BlockRun and Incarna use Amazon Bedrock AgentCore payments for per-inference agent billing
Amazon Bedrock AgentCore payments lets AI agents pay for services on demand with infrastructure-enforced spending limits. Incarna's agents pay BlockRun for model inference one request at a time over x402, reducing the work of adding x402 payment support from months to days, according to an AWS Machine Learning Blog post.