Official announcement · AWS Machine Learning ·

Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs

AWS Machine Learning

AWS Batch quota management is a new scheduling capability you can use to divide GPU capacity into quota shares per team or project, automatically lend idle capacity across shares, and use preemption to run your highest-priority SageMaker Training jobs first, all without manual coordination.

Read at aws.amazon.com The opening of the original, or for stories before 30 October 2026 an AI model's summary of it. Check the original before you rely on a detail.

In this story

A tool or model opens in the Heidelberg AI Curriculum: what it does, what it costs, how to start. A repo opens on GitHub.

More from AWS Machine Learning

All stories
AWS Machine Learning

OfficialAWS Machine Learning

Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams

This post presents a five-stage operating model for managing Amazon Bedrock cost: attribute every dollar to an owner, make every request observable, enforce budgets, optimize platform defaults, and operate a review cadence. Written for FinOps and platform engineering teams.

AWS Machine Learning

OfficialAWS Machine Learning

How Worldwide Express automated freight exceptions with Amazon Bedrock

Worldwide Express automated its less-than-truckload freight billing-exception process with a multi-agent solution built on Amazon Bedrock. Six months after launch, the company cut active exceptions by 75%, improved time-to-cash by four weeks, and unlocked $10 million in free cash flow.