model-deployment

1# Model Deployment
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3Identifies the correct deployment pathway based on model characteristics and generates deployment code.
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5## Scope
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7This skill supports deploying Nova and OSS models that were fine-tuned through **SageMaker Serverless Model Customization** only.
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9**Not supported:**
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11- Base models (not fine-tuned)
12- Models fine-tuned through other processes
13- Full Fine-Tuning (FFT) — only LoRA fine-tuned models are supported
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15## Prerequisites
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17- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the `sdk-getting-started` skill first.
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19---
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21## Principles
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231. **One thing at a time.** Each response advances exactly one decision.
242. **Confirm before proceeding.** Wait for the user to agree before moving on. But don't re-ask questions already answered in the conversation — use what you know.
253. **Don't read files until you need them.** Only read pathway references after the pathway is confirmed.
264. **Use what you know.** If conversation history or artifacts already answer a question, confirm your understanding instead of asking again.
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28## Workflow
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30### Step 1: Identify the Training Job
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32You need the training job name or ARN. Check the conversation history first — the user may have already mentioned it, or it may be available from earlier steps in the workflow (e.g., fine-tuning). If not, ask the user.
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34Once you have the training job name or ARN, use the AWS MCP tool to look it up:
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