SageMaker AI now supports serverless model customization for Qwen3.6

Amazon SageMaker AI Updates

Amazon SageMaker AI now supports serverless model customization for Qwen3.6 27B parameter model using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). This update adds to the existing support for fine-tuning Qwen3.5 and other popular models.

Model customization allows you to adapt foundation models to your specific domains and workflows using your proprietary data. This process enables more accurate reflection of domain knowledge, terminology, and quality standards. Serverless customization means SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation.

This feature is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To start, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access.

What to do

• Launch a customization job from the Models page in Amazon SageMaker Studio.
• Use the SageMaker Python SDK for programmatic access.

Source: AWS release notes

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