Model Deployment

AWS LabsOfficialPublished by AWS Labs

Skill
Workflow
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

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

Download the skill as a ZIP:
https://autogineer.com/agent-skills/awslabs/model-deployment/download

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

One thing at a time. Each response advances exactly one decision.
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.
Don't read files until you need them. Only read pathway references after the pathway is confirmed.
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:

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
Use the AWS MCP tool list-tags on the training job ARN and extract:
- Model ID from the sagemaker-studio:jumpstart-model-id tag
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 Type	Eligible Targets
OSS	SageMaker, Bedrock
Nova	SageMaker, 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.

Read references/model-licenses.md and look up the model by its model ID (determined in Step 1).
Follow the instructions in the Notes column — use the exact phrasing provided.
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 Type	Deployment Target	Reference
OSS	SageMaker	`references/deploy-oss-sagemaker.md`
OSS	Bedrock	`references/deploy-oss-bedrock.md`
Nova	SageMaker	`references/deploy-nova-sagemaker.md`
Nova	Bedrock	`references/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

ATTRIBUTION.mdAdded by AutoGineer916 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer89 B
SKILL.md5.8 KiB
code_templates/deploy-nova-bedrock.pyScript1.9 KiB
code_templates/deploy-nova-sagemaker.pyScript1.7 KiB
code_templates/deploy-oss-bedrock.pyScript3.9 KiB
code_templates/deploy-oss-sagemaker.pyScript1.9 KiB
references/code_output_guide.md3.2 KiB
references/deploy-nova-bedrock.md4.0 KiB
references/deploy-nova-sagemaker.md5.0 KiB
references/deploy-oss-bedrock.md4.7 KiB
references/deploy-oss-sagemaker.md6.5 KiB
references/model-licenses.md11.5 KiB

references/deploy-nova-bedrock.md

Deploy Nova LoRA to Bedrock (PySDK BedrockModelBuilder)

Scenario

Model Type: Nova
Fine-tuning Method: LoRA
Deployment Target: Bedrock Custom Model
Approach: SageMaker PySdk BedrockModelBuilder

Overview

Uses the SageMaker PySdk BedrockModelBuilder to deploy a Nova fine-tuned LoRA model to Bedrock as a Custom Model. The builder auto-detects Nova models and calls CreateCustomModel.

Required inputs (collected in the steps below):

Training job name
Custom model name
IAM role ARN
AWS region

Prerequisites

Requires SageMaker Python SDK >= 3.7.0 with BedrockModelBuilder Nova support (installed by Cell 1).

Workflow

Important Instructions

Make sure to use dedicated tools instead of bash commands whenever possible

Step 1: Gather Training Job Name

The training job name was identified in Step 1 of the main workflow. Confirm you have it.

Step 2: Gather Custom Model Name

For this step, you need: a name for the deployed custom model.

Suggest a name based on the training job or use case, e.g., nova-micro-bedrock-<timestamp>. Ask the user to confirm or provide their own.

⏸ Wait for user before moving on.

Step 3: Verify IAM Role

Use the IAM role from the training job (extracted in Step 1 of the main workflow via describe-training-job). We'll assume this role has the necessary permissions for Bedrock deployment.

Step 4: Confirm Region

The region was identified in Step 1 of the main workflow. Nova → Bedrock deployment is currently only supported in us-east-1. If the training job is in a different region, tell the user that Bedrock deployment is not supported for this model in this region.

Step 5: Confirm Configuration

"Here's the deployment setup:
Deployment target: Bedrock (Custom Model)
Training Job: [job-name]
Custom Model Name: [name]
IAM Role: [arn]
Region: us-east-1
Does this look right?"

⏸ Wait for user approval.

Step 6: Generate Code

Read ../references/code_output_guide.md for output format rules.

If a project directory already exists (from earlier in the workflow), use it. Otherwise, activate the directory-management skill to set one up.

⏸ Wait for user.

Code Structure

Markdown Header

{
  "cell_type": "markdown",
  "metadata": {},
  "source": [
    "# Deploy Nova to Bedrock"
  ]
}

Cells

Each cell's content comes from ../code_templates/deploy-nova-bedrock.py, split on the # Cell N: comments. Each marker starts a new notebook cell — everything between one marker and the next becomes that cell's content.

Cell 1: Setup (pip install)
Cell 2: Configuration (env vars, imports, placeholders)
Cell 3: Build and Deploy to Bedrock (blocks until deployment is Active)
Cell 4: Test Inference

Placeholders

Cell 2:

[REGION] → AWS region (us-east-1)
[TRAINING_JOB_NAME] → SageMaker training job name
[ROLE_ARN] → IAM role ARN
[CUSTOM_MODEL_NAME] → Name for the custom model

All other cells have no placeholders.

Step 7: Provide Run Instructions

To run:
1. Cell 1 — install SDK packages
2. Cell 2 — set configuration values
3. Cell 3 — creates custom model via BedrockModelBuilder and deploys (blocks until Active)
4. Cell 4 — test inference with a sample prompt via Converse API

Common Issues

"ServiceQuotaExceededException: The number of custom models in Creating status has reached the quota limit": Too many concurrent model creations. Wait for in-progress models to finish, or delete old custom models.
"No module named 'sagemaker.serve.bedrock_model_builder'": Re-run Cell 1 to install the required packages, then restart the kernel.
"Access denied to S3": Add S3 read permissions to the IAM role for the model artifacts bucket.
"Provided IAM role could not be assumed": Ensure role has trust policy for bedrock.amazonaws.com.
Deployment status "Failed": Check CloudTrail for the CreateCustomModel event to see the failure reason.

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)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by AWS Labs or AWS Labs.

Download ZIP

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

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by AWS Labs

Model Deployment

Scope

Prerequisites

Principles

Workflow

Step 1: Identify the Training Job

Step 2: Determine Eligible Deployment Targets

Step 3: Let the User Choose a Deployment Target

Step 4: Display License Agreement

Step 5: Follow Pathway Workflow

Step 6: Post-Deployment Summary

Troubleshooting

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

Files

references/deploy-nova-bedrock.md

Deploy Nova LoRA to Bedrock (PySDK BedrockModelBuilder)

Scenario

Overview

Prerequisites

Workflow

Important Instructions

Step 1: Gather Training Job Name

Step 2: Gather Custom Model Name

Step 3: Verify IAM Role

Step 4: Confirm Region

Step 5: Confirm Configuration

Step 6: Generate Code

Code Structure

Markdown Header

Cells

Placeholders

Step 7: Provide Run Instructions

Common Issues

Requirements and compatibility

Security

Attribution and licence

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Download ZIP