model-deployment

# Model Deployment Identifies the correct deployment pathway based on model characteristics and generates deployment code. ## Scope This skill supports deploying Nova and OSS models that were fine-tuned through **SageMaker Serverless Model Customization** only. **Not supported:** - Base models (not fine-tuned) - Models fine-tuned through other processes - Full Fine-Tuning (FFT) — only LoRA fine-tuned models are supported ## Principles 1. **One thing at a time.** Each response advances exactly one decision. 2. **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. 3. **Don't read files until you need them.** Only read pathway references after the pathway is confirmed. 4. **Use what you know.** If conversation history or artifacts already answer a question, confirm your understanding instead of asking again. ## Workflow ### Step 1: Identify the Training Job You 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. Once you have the training job name or ARN, use the AWS MCP tool to look it up: 1. Use the AWS MCP tool `describe-training-job` and extract: - **S3 output path** (from `ModelArtifacts.S3ModelArtifacts` or `OutputDataConfig.S3OutputPath`) - **IAM role ARN** (from `RoleArn`) - **Region**...