Announcing new ways to customize models with SageMaker AI, without managing GPUs

Key points

• They’re customizing open-weight models using post-training techniques such as reinforcement learning.1
• 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.1
• SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage.1
• With SageMaker AI serverless model customization, you don’t choose instance types, manage clusters, or write distributed training configuration.1

Sources (1)

• [1]Announcing new ways to customize models with SageMaker AI, without managing GPUs
  AWS Machine Learning Blog · Oct 22, 06:00 PM

  Companies getting real results from AI aren’t only using foundation models (FMs) off the shelf.

  They’re customizing open-weight models using post-training techniques such as reinforcement learning.

Extractive summary: sentences quoted from the sources.