Amazon SageMaker AI now supports serverless model customization for Gemma 4 E4B and 31B models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT). Gemma is a family of open models built by Google DeepMind. In addition to deploying these models on SageMaker AI, you can now adapt them to your specific domains and workflows. This launch also extends the variety of models available for serverless customization on SageMaker AI, including models from the Nova, Nemotron 3, Qwen, Llama, gpt-oss, and DeepSeek families.
Model customization enables you to tailor these foundation models with your proprietary data, whether that’s improving accuracy on domain-specific tasks, aligning outputs with your organization’s tone, or enhancing performance on new tasks using your labeled data. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use.
Serverless model customization on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access. To learn more, see the Amazon SageMaker AI model customization documentation.
Categories: general:products/amazon-sagemaker,marketing:marchitecture/artificial-intelligence
Source: Amazon Web Services
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