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Model Deployment

AWS LabsOfficialPublished by AWS Labs

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts
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From the author

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.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill guides users through the deployment process for LoRA fine-tuned Nova and OSS models originally trained via SageMaker Serverless Model Customization. It identifies the correct deployment pathway, verifies model eligibility, presents target options (SageMaker or Bedrock), displays relevant license terms, and generates the necessary Python code or Jupyter notebooks to execute the deployment.

What it does

  • Identifies training job details and model types from SageMaker metadata.
  • Determines eligible deployment targets based on model characteristics.
  • Displays required license agreements for the specific model.
  • Generates deployment code as either a Python script or Jupyter notebook.
  • Provides post-deployment summaries including invocation samples and cleanup instructions.

When to use it

  • When you need to deploy a LoRA fine-tuned model to a SageMaker endpoint.
  • When you want to make a fine-tuned model available via Amazon Bedrock.
  • When you need to generate deployment code for Nova or OSS models.
  • When you are unsure which deployment target is appropriate for your fine-tuned model.

Advantages

  • Automates the identification of deployment pathways.
  • Ensures consistent configuration through standardized code templates.
  • Provides clear, step-by-step guidance for complex AWS deployments.
  • Integrates license verification into the deployment workflow.

Usage examples

“Deploy my fine-tuned model.”
The agent will ask for the training job name, identify the model type, and guide you through selecting a deployment target.
“Create an endpoint for my Nova model.”
The agent will verify the model's eligibility, display the license, and generate the deployment code for SageMaker or Bedrock.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/awslabs/model-deployment/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip model-deployment.zip -d .agents/skills

Documentation by AWS Labs

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

Unsupported models: This skill only supports OSS and Nova models that were LoRA fine-tuned through SageMaker Serverless Model Customization. If the model doesn't match, tell the user this skill can't help and suggest the finetuning skill.

Step 2: Determine Eligible Deployment Targets

Use the following table:

Model TypeEligible Targets
OSSSageMaker, Bedrock
NovaSageMaker, Bedrock

If only one target is eligible, confirm it with the user. Use details from Step 5.

If multiple targets are eligible, help the user decide. Use details from Step 5.

If no targets are eligible, tell the user and explain why.

Step 3: Let the User Choose a Deployment Target

Present the eligible options to the user. Present these details to help them decide between SageMaker and Bedrock, if both are available options:

SageMaker Endpoint:

  • Dedicated compute resources for consistent performance

  • Control instance types and scaling

  • Best for predictable workloads with specific latency requirements

Bedrock:

  • Fully managed serverless inference

  • Auto-scales instantly with no capacity planning

  • Pay per request

  • Best for variable workloads with fluctuating demand

Do NOT make a recommendation. Let the user choose.

Do NOT mention technical details like merged/unmerged weights, reference files, or APIs, unless the user asks.

⏸ Wait for user to select a deployment option.

Step 4: Display License Agreement

Before proceeding to deployment, display the model's license or service terms to the user.

  1. Read references/model-licenses.md and look up the model by its model ID (determined in Step 1).

  2. Follow the instructions in the Notes column — use the exact phrasing provided.

  3. If the model ID is not found in the table, warn the user that you could not find license information for their model and recommend they verify the license independently before proceeding.

⏸ Wait for the user to confirm before proceeding.

Step 5: Follow Pathway Workflow

Read the reference file for the selected pathway and follow its instructions.

Model TypeDeployment TargetReference
OSSSageMakerreferences/deploy-oss-sagemaker.md
OSSBedrockreferences/deploy-oss-bedrock.md
NovaSageMakerreferences/deploy-nova-sagemaker.md
NovaBedrockreferences/deploy-nova-bedrock.md
Step 6: Post-Deployment Summary

After deployment completes, provide the user with a summary. Cover these topics, using details from the pathway reference doc you followed in Step 5:

  • What was deployed — endpoint or model name, ARN, status

  • How to use it — sample invoke code for the specific deployment target

  • Cost — billing model (instance-based vs. pay-per-request) and what to expect

  • Cleanup — how to delete the endpoint or model when done

Troubleshooting

How to check if a model was LoRA or FFT fine-tuned

If deployment fails unexpectedly, the model may have been full fine-tuned (FFT) rather than LoRA. To check, download the training job's hydra config from its S3 output path at .hydra/config.yaml:

  • peft_config populated (r, alpha, dropout, etc.) → LoRA (supported)

  • peft_config: null → FFT (not supported by this skill)

Files

references/code_output_guide.md

Code Output Guide

Mode Selection

Ask the user once before generating code: "Would you like me to generate a Jupyter notebook or a Python script?"

If the output format has already been decided in the conversation context, keep consistent — do not re-ask.

Shared Rules (Both Modes)

  • Use EXACTLY the imports shown in each code template — do not add extras

  • Replace [PLACEHOLDER] values with user-specific configuration

  • Include set_attribution(Attribution.SAGEMAKER_AGENT_PLUGIN) in the setup cell/section

Reading Code Templates

Templates use # Cell N: Label markers to delimit sections. # NOTEBOOK_ONLY skips a line in script mode; # NOTEBOOK_ONLY_SECTION on a # Cell N: line skips the entire section.

Notebook Mode

Write a .ipynb file in <project-dir>/notebooks/.

Naming and appending:

  • Notebook path: <project-dir>/notebooks/<project-name>.ipynb

  • If the notebook already exists → ask: "Would you like me to append cells to the existing notebook, or create a new one?"

  • If it doesn't exist → create it

  • When appending, use the template's # Cell 0 [markdown]: cell as the section divider before the new cells

Formatting:

  • Use your file write tool to create the complete notebook JSON, OR use notebook MCP tools (create_notebook, add_cell) if available

  • Do NOT use bash commands, shell scripts, or echo/cat piping

  • 2-space JSON indentation

  • Each source line is a separate string ending with \n (except the last)

  • Escape quotes: \"

  • No trailing commas

Structure:

  • Wrap cells in {"cells": [...], "metadata": {...}, "nbformat": 4, "nbformat_minor": 4}

  • Code cells: cell_type, execution_count: null, metadata: {}, outputs: [], source: [...]

  • Markdown cells: cell_type: "markdown", no execution_count or outputs

  • # Cell 0 [markdown]: becomes a markdown cell; all others become code cells

Execution:

  • If notebook execution tools are available (e.g., run_cell MCP), offer to run cells for the user. If not available, tell the user to run cells themselves.

  • Do NOT use bash commands or inline scripts to execute notebook cells.

Script Mode

Write a numbered .py file in <project-dir>/scripts/.

Naming:

  • Format: NN_<descriptive_name>.py (e.g., 01_sft_finetuning.py) — use the next available number in <project-dir>/scripts/

Formatting:

  • Plain Python file, standard text

  • Use # %% cell markers to preserve logical sections (IDE-compatible)

  • Include a docstring at the top describing what the script does

  • # Cell 0 [markdown]: → a comment block or docstring

Dependencies:

  • Install any required pip packages directly (e.g., pip install sagemaker>=3.7.1) before writing or running the script. Do not embed install commands in the script itself.

Execution:

  • Run the script using standard Python execution (python3 <script>.py).

Resumption After Interruption

If the conversation was interrupted while a job was running (e.g., context compaction, user stopped and restarted, connection drop), do NOT re-run the script. Instead, check for an existing job by name or ARN from the conversation context or PLAN.md, and monitor its status rather than launching a duplicate.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS Labs
Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/sagemaker-ai/skills/model-deployment · commit fbefb37
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download model-deployment.zip