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AWS launches training sessions for SageMaker AI model customization

AWS adds training sessions to SageMaker AI serverless model customization, letting developers control post-training loops without managing GPUs.

AWS launches training sessions for SageMaker AI model customization — image from AWS
Image: AWS

Summary

AWS announced the general availability of training sessions for SageMaker AI serverless model customization. The new feature lets developers write custom post-training loops without configuring underlying GPU infrastructure. SageMaker AI automatically handles GPU allocation, distributed execution, fault recovery, and checkpoint storage.

What changed

  • Launched training sessions for SageMaker AI serverless model customization to handle post-training logic without managing GPU infrastructure.
  • Added support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B models.
  • Handled GPU allocation, distributed execution, fault recovery, and checkpoint storage automatically.

Availability

Available now

“We launched Amazon SageMaker AI serverless model customization to solve these challenges, and today, we’re expanding it with the general availability launch of training sessions.”

Founder takeaway

If you customize open-weight AI models, evaluate SageMaker AI training sessions to offload GPU infrastructure management while keeping control over your training loops.

Source

Researched and drafted with AI from the official sources above, then checked automatically before publishing. Spotted a mistake? Tell us.

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