Amazon brings agentic fine-tuning to SageMaker with support for Llama, Qwen, Deepseek, and Nova
Amazon SageMaker AI now includes an AI agent that lets developers describe use cases in plain language, automatically recommends training methods, prepares data, kicks off training, and delivers editable Jupyter notebooks. Supports Llama, Qwen, Deepseek, and Nova model families.
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Key points
- SageMaker AI introduces the Kiro AI agent for automated fine-tuning via natural language.
- The agent is preinstalled in the development environment; alternative agents like Claude Code can be used.
- Supports Llama, Qwen, Deepseek, and Amazon's own Nova model families.
- All generated code is editable and reusable.
Why it matters
This matters because sageMaker AI introduces the Kiro AI agent for automated fine-tuning via natural language.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
Amazon brings agentic fine-tuning to SageMaker with support for Llama, Qwen, Deepseek, and Nova
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Amazon brings agentic fine-tuning to SageMaker with support for Llama, Qwen, Deepseek, and Nova
Maximilian Schreiner
View the LinkedIn Profile of Maximilian Schreiner
May 5, 2026
Amazon SageMaker AI now includes an AI agent designed to help developers customize language models. SageMaker AI is Amazon's cloud platform for building, training, and deploying machine learning models.
Instead of wrestling with different APIs and data formats, developers can now describe their use case in plain language. The agent then recommends the right training method, prepares the data, kicks off training, and delivers the finished code as Jupyter notebooks.
Amazon's Kiro AI agent comes preinstalled in the development environment, but developers can also use Claude Code or other agents. Nine prebuilt "skills" handle the workflow, from checking the dataset to deploying the finished model. The agent supports model families like Llama, Qwen, Deepseek, and Amazon's own Nova. All generated code is editable and reusable.
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Source: AWS
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