SPIN Unprocessed October 22, 2026 ai_technology enterprise_ai
Announcing new ways to customize models with SageMaker AI, without managing GPUs
View original on aws.amazon.comOverview
Amazon SageMaker AI serverless model customization now includes training sessions, generally available today. You write and coordinate your own post-training loop (custom loss functions, reward signals, or multi-turn reinforcement learning) while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage.
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from AWS Machine Learning Blog
View all →- Workflow context for AI agents with Smartsheet MCP on AWS
- How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases
- Amazon Polly delivers a major upgrade to its Generative engine
- How Rescale built an AI triage agent with Strands Agents and AI-DLC
- Stop guessing auto scaling targets for TTS on Amazon SageMaker AI
- Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO