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

Prerequisites

• 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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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
2. Use the AWS MCP tool list-tags on the training job ARN and extract:
   • Model ID from the sagemaker-studio:jumpstart-model-id tag
3. Determine the model type from the model ID:
   • Contains "nova" (nova-micro, nova-lite, nova-pro) → Nova
   • Llama, Mistral, Qwen, GPT-OSS, DeepSeek, etc. → OSS