# Model Deployment

Generates code that deploys fine-tuned models from SageMaker Serverless Model...

- Repository: https://github.com/awslabs/agent-plugins (933 stars)
- Category: DevOps
- License: Apache-2.0
- Last updated: 2026-10-15
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add awslabs/agent-plugins --skill model-deployment -g -a claude-code -y`
Codex: `npx skills add awslabs/agent-plugins --skill model-deployment -g -a codex -y`
Cursor: `npx skills add awslabs/agent-plugins --skill model-deployment -g -a cursor -y`
Gemini CLI: `npx skills add awslabs/agent-plugins --skill model-deployment -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add awslabs/agent-plugins`

## Description

Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

---

## 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**

The rest: https://github.com/awslabs/agent-plugins/blob/HEAD/plugins/sagemaker-ai/skills/model-deployment/SKILL.md