---
type: "post"
id: "announcing-new-ways-to-customize-models-with-sagemaker-030e9c"
slug: "announcing-new-ways-to-customize-models-with-sagemaker-030e9c"
title: "Announcing new ways to customize models with SageMaker AI, without managing GPUs"
description: "AWS has made Training Sessions generally available within SageMaker AI Serverless Model Customization."
retrieval_nugget: "AWS has made Training Sessions generally available within SageMaker AI Serverless Model Customization."
published_at: "2026-10-22"
updated_at: "2026-10-22"
record_date: "2026-10-22"
date_kind: "published_at"
topics: ["claude","claude-code","codex"]
entities: ["Amazon Web Services","Amazon SageMaker AI","Training Sessions","Kiro","Claude Code","Codex","GitHub Copilot"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "AWS announces SageMaker AI model-customization training sessions that require no GPU management; the archive covers JumpStart updates, not this capability."
prior_refs: ["memory:06910318-cb8f-8445-a1c8-b4dcef2af20b"]
source_url: "https://aws.amazon.com/blogs/machine-learning/announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus"
source_urls: ["https://aws.amazon.com/blogs/machine-learning/announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus"]
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stable_id: "post:announcing-new-ways-to-customize-models-with-sagemaker-030e9c"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

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

## Retrieval answer

AWS has made Training Sessions generally available within SageMaker AI Serverless Model Customization.

AWS has made Training Sessions generally available within SageMaker AI Serverless Model Customization. The service lets customers write and coordinate their own post-training loop while AWS manages GPU allocation, distributed execution, fault recovery, and checkpoint storage.

AWS says users can supply custom loss functions and reward signals, including for multi-turn reinforcement learning. The announced workflow supports techniques including supervised fine-tuning, DPO, RLVR, RLAIF, and multi-turn RL, with either LoRA or full fine-tuning. AWS also describes continuous customization, which can chain techniques together.

The company says its cookbook and model-customization agent skills can assist with data preparation, technique selection, training, evaluation, and deployment. It lists SageMaker Studio, Kiro, Claude Code, Codex, and GitHub Copilot as working environments. The announcement does not establish pricing, regional availability, compute limits, supported-model coverage, or independently measured quality or cost results. Source: https://aws.amazon.com/blogs/machine-learning/announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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