AWS AI ML

AWSOfficialPublished by AWS

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

From the author

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

— the description by AWS

AutoGineer summary

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

This skill provides a structured framework for managing the Amazon SageMaker model lifecycle. It covers planning, dataset preparation, fine-tuning, model evaluation, and endpoint deployment. The skill assists users in navigating SageMaker-specific workflows, including selecting base models from catalogs, configuring training and processing jobs, and diagnosing endpoint health. It supports various fine-tuning techniques and deployment pathways, ensuring users follow validated procedures for their machine learning projects on AWS.

What it does

Guides users through the end-to-end lifecycle of SageMaker model customization.
Assists in selecting and evaluating base models from SageMaker JumpStart and public hubs.
Provides workflows for validating and transforming training and evaluation datasets.
Supports configuration of fine-tuning techniques like SFT, DPO, RLVR, and RLAIF.
Facilitates the deployment of models to SageMaker endpoints or Bedrock.
Diagnoses endpoint issues including latency, errors, and container logs.

When to use it

When planning a new model customization or deployment project on SageMaker.
When you need to validate or transform datasets for training or evaluation.
When selecting a base model or choosing a fine-tuning technique.
When you need to configure training, processing, or evaluation jobs.
When deploying a model to a SageMaker endpoint or troubleshooting an existing one.
When setting up or managing a SageMaker Managed MLflow environment.

Advantages

Provides structured, step-by-step workflows for complex SageMaker tasks.
Ensures dataset formatting meets specific SageMaker requirements.
Offers clear routing to relevant documentation based on user intent.
Helps resolve configuration issues for IAM, S3, and SDK settings.
Simplifies the diagnosis of endpoint performance and failure logs.

Usage examples

“I need to fine-tune a model on SageMaker. Where do I start?”: The agent will initiate the planning workflow, prompting you to define your use case and requirements before guiding you through model selection and dataset preparation.
“My SageMaker endpoint is returning errors. Can you help?”: The agent will route you to the endpoint diagnostics workflow to collect logs, check instance status, and analyze latency or failure metrics.
“How do I convert my CSV training data for SageMaker?”: The agent will guide you through the dataset transformation workflow, recommending the appropriate tools to convert your data into the required JSONL format.
“Which base model should I use for a text summarization task?”: The agent will trigger the model selection workflow to help you evaluate available models and resolve the correct Hub model ID for your specific requirements.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/aws-ai-ml/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip aws-ai-ml.zip -d .agents/skills
```

Documentation by AWS

AWS AI/ML Model Customization

Domain expertise for fine-tuning and deploying models on Amazon SageMaker. Covers the full model customization lifecycle from planning through production deployment.

Routing

Match the user's intent to the appropriate reference folder and load only that content.

User intent	Reference	When to use
Plan a model customization project, discover scope of work, resume or modify a plan	references/planning/	User's request relates to model customization or deployment (fine-tuning, training, building, customizing, reviewing data, deploying or standing up a model — including selecting or deploying an off-the-shelf or base model with no training — or getting advice on approach). Always co-activate with other intents to discover full scope. Load this reference FIRST when the request matches multiple rows in this table — read its plan templates before routing to a single-action reference.
Define the business problem, success criteria, or use case spec	references/use-case-specification/	User says "define my use case", "capture requirements", "what should I decide up front", or as default first step in any plan. Skip only if user explicitly declines.
Select or change a base model	references/model-selection/	User asks which model to use, mentions a model name or family, or wants to evaluate what's available. Always activate model-selection even for known model names because the exact Hub model ID must be resolved. Recommended: route to use-case-specification first to capture requirements — this produces better filtering results. Routing to use-case-specification first is not required if user provides a specific model name/ID or declines. If intent is ambiguous (fine-tune vs deploy as-is), model-selection MUST confirm which path before proceeding. Base model filtering for deployment MUST go through select-for-deployment.md and its scripts for any final recommendation.
Choose a fine-tuning technique (SFT, DPO, RLVR, RLAIF)	references/finetuning-technique/	User has decided to fine-tune and needs to choose a technique, or technique needs validation against the selected model's recipes. Requires a base model to be selected first.
Validate dataset quality and format	references/dataset-evaluation/	User says "is my dataset okay", "check my training data", "I have my own data", or before starting any fine-tuning job.
Transform or convert a dataset between formats	references/dataset-transformation/	User says "transform", "convert", "reformat", or dataset schema needs to change. Always use this rather than writing inline transformation code.
Generate fine-tuning code and start training	references/finetuning/	User says "start training", "fine-tune my model", "I'm ready to train", or plan reaches the finetuning step. Supports SFT, DPO, RLVR, RLAIF trainers.
Train a model with a SageMaker Training job	references/training-jobs/	Use when the goal is to fit or update model parameters and produce model artifacts or checkpoints. Covers recipes, JumpStart traditional ML, custom training scripts or containers, distributed training, and training-job diagnosis.
Process data with a SageMaker Processing job	references/processing-jobs/	Use when the goal is preprocessing, feature engineering, evaluation, transformation, or ETL without model training as the primary operation. Covers scripts, framework processors, Spark, custom containers, Athena, Redshift, and processing-job diagnosis.
Evaluate or benchmark a trained model	references/model-evaluation/	User says "evaluate my model", "run a benchmark", "test model performance", "compare models". Supports LLM-as-Judge and Custom Scorer.
Deploy, benchmark, or optimize a model on an endpoint or Bedrock	references/model-deployment/	User says "deploy my model", "create an endpoint", or "make it available" (plain deploy) — or, for the inference-optimization sub-workflows on SageMaker Real-Time Endpoints only, "benchmark my endpoint" / "compare benchmark runs" (benchmarking), or states a performance/cost/latency/throughput goal for a new deployment such as "find the cheapest instance" (recommendations). Handles Nova vs OSS deployment pathways.
Set up IAM roles, S3 buckets, SDK configuration	references/sdk-getting-started/	User says "set up", "getting started", "check my environment", "configure SDK", or as first step in any plan involving SageMaker training/evaluation/deployment.
Manage project directory and artifacts	references/directory-management/	Starting a new project, resuming existing one, or when PLAN.md needs to be associated with a project directory.
Set up, update, or delete a SageMaker Managed MLflow app	references/manage-mlflow/	User says "set up MLflow", "create MLflow app", "update my MLflow app", "delete my MLflow app", "I need an MLflow server", asks "what is SageMaker MLflow", or a workflow needs an MLflow backend and none is connected.
Diagnose a failing or unhealthy SageMaker endpoint	references/endpoint-diagnostics/	User reports endpoint errors, latency, inference failures, or a deployment that failed. "What's the status of my endpoint?", "Is my endpoint erroring?", "My endpoint failed — why?", "How many instances are running behind my endpoint?", "Is the latency my model or SageMaker?", "Show me the container logs for my endpoint." NOT for training-job issues, endpoint deletion, scaling changes, or new deployments.

Rules

Service boundary. This skill covers SageMaker, including deployment of a customized model to Bedrock. Bedrock-native work (invoking Bedrock's managed foundation models, Bedrock Agents, Knowledge Bases, Guardrails, AgentCore) is supported by AWS but has no validated workflow in this skill: let the user know and proceed with best-effort guidance using general AWS knowledge. If the user asks for a model on AWS without naming a service, compare SageMaker and Bedrock or ask one service-choice question. Do not select a service by default. The two services have different model catalogs, deployment paths, and pricing, and a default selection can send the user into an incorrect workflow.
Progressive disclosure. Load only the reference folder relevant to the current user intent. Do not load all references at once.
Best-effort help. If the user's request falls outside this skill's references, do not dead-end the conversation. Help them using general AWS knowledge and documentation, and inform the user that the guidance is not covered by this skill's validated workflows.
Usage attribution. Before running any AWS CLI command or packaged script, set export AWS_SDK_UA_APP_ID=AWSSkill-SageMaker.

Files

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references/dataset-transformation/overview.md

Dataset Transformation Agent

Transforms a data set provided by the user into their desired format.

When to Use

User needs to generate code for transforming datasets for SageMaker model training or model evaluation.
A dataset requires processing, cleaning, or formatting before training or evaluation.
Workflow requires a formal review and approval cycle before execution.

Prerequisites

The SDK environment has been verified (SDK version, region, execution role). If not done, load the sdk-getting-started reference first.

Principles

One thing at a time. Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn.
Confirm before proceeding. Wait for the user to agree before moving to the next step. You are a guide, not a runaway train.
Don't read files until you need them. Only read reference files when you've reached the workflow step that requires them and the user has confirmed the direction. Never read ahead.
No narration. Don't explain what you're about to do or what you just did. Share outcomes and ask questions. Keep responses short and focused.
No repetition. If you said something before a tool call, don't repeat it after. Only share new information.
Do not deviate from the Workflow. The steps listed in the workflow should be followed exactly as described. Progress from Step 1 to Step 11 to complete the task. Do not deviate from the workflow!
Always end with a question. Whenever you pause for user input, acknowledgment, or feedback, your response must end with a question. Never leave the user with a statement and expect them to know they need to respond.
Default output format is JSONL. Unless the user explicitly requests a different file format, the transformed dataset should be written as .jsonl (JSON Lines — one JSON object per line).

Known Dataset Formats Reference

This skill supports two transformation purposes — training data and evaluation data — each with its own format resolution path. The purpose is determined in Step 1 of the workflow.

Training Data Formats

Resolve the target format using the reference file ../dataset-evaluation/references/strategy_data_requirements.md. When the transformation is for model training, the required format depends on both the model type (Open Weights like Llama/Qwen vs Nova) and the finetuning technique (SFT, DPO, RLVR, RLAIF) — make sure to match on both dimensions. If either the model type or technique is not yet known, ask the user before resolving the format.

Evaluation Data Formats

When the transformation is for model evaluation, resolve the target format using this order:

Try fetching the live documentation at https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-evaluation-dataset-formats.html to get the latest evaluation dataset schema definitions.
If the fetch fails (e.g., no internet access, VPC environment), fall back to the offline copy at references/sagemaker_dataset_formats.md. Inform the user that the format schemas are from an offline copy and may be outdated.

Use whichever source you successfully access as the source of truth for the target format. Do not rely on memorized schemas.

Workflow

Step 1: Determine transformation purpose

Your first response should determine whether this transformation is for model training or model evaluation. If the context already makes this clear (e.g., the user said "I need to prep my training data" or "I need to format my eval dataset"), confirm your understanding and move on. Otherwise, ask:

"Is this dataset transformation for model training or model evaluation? This helps me look up the right target format for you."

Training → format resolution will use the local training data requirements reference (model type + finetuning technique dependent).
Evaluation → format resolution will use the live AWS documentation (with offline fallback).

Remember this choice — it determines how the target format is resolved in Step 3.

⏸ Wait for user.

Step 2: Set expectations

Acknowledge the user's request and state what this skill can do:

"I can help you transform your dataset's format! Here's my plan: I will first need to understand the format of your dataset and the transformation requirements. Once I have that, I will generate a dataset transformation function that we can refine together. After the dataset transformation function is refined to your liking, I will perform the transformation task and upload it to your desired location! Does this sound good?"

⏸ Wait for user.

Step 3: Understand the dataset transformation task

For this step, you need to know: what dataset format the user would like to transform their dataset from and what dataset format they would like to transform it in to. If you know this already, skip this step. If not, ask the user:

"What's the dataset format you would like to transform it into?"

Resolve the target format based on the purpose determined in Step 1:

If training data: Ask the user for the finetuning technique (SFT, DPO, RLVR, RLAIF) and model type (Open Weights like Llama/Qwen vs Nova) if not already known. Then look up the required format from the "Training Data Formats" section in the Known Dataset Formats Reference above.
If evaluation data: If the user mentions a well-known format name (e.g., "OpenAI format", "SageMaker format"), fetch the schema from the live documentation as described in the "Evaluation Data Formats" section above. If a well-known format is fetched, confirm with the user:

"I've found a SageMaker dataset format: {sagemaker-dataset-format-name} with schema: {sagemaker-dataset-format-schema}. Is this what you were referring to?"

If the user describes a custom format not listed in the reference doc, ask them to provide a sample record of the desired output format.

⏸ Wait for user.

Step 4: Get the dataset from the user

For this step, you need: the location of the user's dataset. If you know this already, skip this step. If not, ask the user:

"Where can I find your dataset? Either a local directory or S3 location works!"

⏸ Wait for user.

Step 5: Examine sample data

Read 1–2 sample records from the user's dataset and show them so the user can confirm the source schema. Do not run format detection — that is handled by the planning reference before this reference is invoked.

Do not show a side-by-side mapping to the target format here — the detailed mapping will be handled in Step 7 when generating the transformation function.

⏸ Wait for user.

Step 6: Get the dataset output location

For this step, you need: to understand where to output the transformed dataset to. It could be an S3 URI or local directory If you already know where the dataset is supposed to be output to, skip this step. If not, ask the user:

"Where should I output your transformed dataset to? Either a local directory or S3 location works!"

If the user provides a directory (not a full file path), construct the output filename using the pattern {original_name}_{target_format}.jsonl (e.g., gen_qa_100k_openai.jsonl).

⏸ Wait for user.

Step 7: Generate and validate the transformation function

For this step, you need: to generate a python function that transforms the dataset from the format in Step 5 to the format in Step 3

Read the reference guide at references/dataset_transformation_code.md and follow its skeleton exactly when generating the transformation function.

The python function should be in the form of:

def transform_dataset(df: pd.DataFrame) -> pd.DataFrame:

The <project-dir> is the project directory established by the directory-management reference (e.g., dpo-to-rlvr-conversion).

In notebook mode, add a %%writefile <project-dir>/scripts/transform_fn.py code cell AND write the file to disk for testing. In script mode, write the file to disk directly.

Continue iterating with the user's feedback — update the code in place on each revision rather than showing code inline.

If sample data was collected in Step 5, test the function against the sample records:

Generate the transformation function.
Write the sample data to a temporary JSONL file (e.g., /tmp/test_input.jsonl), then run:
python3 -c "import sys; sys.path.insert(0, '<project-dir>/scripts'); from transform_fn import transform_dataset; import pandas as pd; df = pd.read_json('/tmp/test_input.jsonl', lines=True); result = transform_dataset(df); print(result.to_json(orient='records', lines=True))"
If the test fails, fix and re-test until it passes.
Show the user the function and transformed sample output for review.

If no sample data, present the function for review and refinement.

⏸ Wait for user.

Step 8: Determine output target

If no project directory exists, load the directory-management reference to set one up.

⏸ Wait for user.

Step 9: Generate the execution code

Before writing the code, read:

references/code_output_guide.md (output format rules)
code_templates/transformation.py (cell structure and skeleton code)

The template uses # Cell N: Label markers — each marker starts a new section. Cell 2 (Transformation Function) is dynamically generated from Step 7; all other cells follow the template skeleton.

Generate the execution logic following the code output guide.

In notebook mode, add a %%writefile <project-dir>/scripts/<script_name>.py code cell AND write the file to disk. In script mode, write the file to disk directly.
The script must import transform_dataset from transform_fn.
Replace placeholders with the actual input/output paths.

Read the reference guide at references/dataset_transformation_code.md and follow its execution script skeleton exactly.

If sample data was collected in Step 5, test the full pipeline:

Write the sample records to a temporary JSONL file (e.g., /tmp/test_input.jsonl).
Run: python3 <project-dir>/scripts/<script_name> --input /tmp/test_input.jsonl --output /tmp/test_output.jsonl
If it fails, debug and fix, then re-run until successful.
Show the user the output for review.

If no sample data, present the notebook for review and refinement.

⏸ Wait for user.

Step 10: Determine and confirm execution mode

Check the size of the input dataset:

If the dataset is in S3, run aws s3api head-object --bucket <bucket> --key <key> and check the ContentLength field.
If the dataset is local, check the file size.

Decision criteria:

Dataset < 50 MB → recommend local execution
Dataset ≥ 50 MB → recommend SageMaker Processing Job

Inform the user of the recommendation and get their approval:

If local:

"Your dataset is {size} MB — since it's under 50 MB, I'd recommend running the transformation locally. Would you like to proceed with local execution, or would you prefer a SageMaker Processing Job instead?"

If SageMaker Processing Job:

"Your dataset is {size} MB — since it's over 50 MB, I'd recommend running this as a SageMaker Processing Job for better performance. Would you like to proceed with a SageMaker Processing Job, or would you prefer to run it locally instead?"

Do not execute until the user approves. If the user rejects the recommendation, switch to the alternative and get their explicit approval before proceeding.

⏸ Wait for user.

After user confirms, add an execution cell to the notebook. Do NOT run the transformation directly (no bash, no inline python). If notebook execution tools (run_cell) are available, offer to run the cells. Otherwise, generate the cell for the user to execute themselves:

If local execution:

Add a cell that runs the transformation by importing from the .py files already on disk (written by the agent during Steps 7 and 9): import transform_dataset from transform_fn, load the dataset, transform, and save output. Scripts are located in <project-dir>/scripts/.

If SageMaker Processing Job:

Add a cell that submits and monitors the Processing Job inline using the V3 SageMaker SDK directly (FrameworkProcessor, ProcessingInput, ProcessingOutput, etc.). Create a FrameworkProcessor with the SKLearn 1.2-1 image, configure inputs/outputs, and call processor.run(wait=True, logs=True) to block the cell and stream logs until the job completes. See scripts/transformation_tools.py for reference implementation details.
Inform the user they can run this cell to kick off and monitor the job.

Important: The agent must NOT execute the transformation directly via bash or inline python. If run_cell is available, use it to run the notebook cells. Otherwise, the cells are for the user to review and run. Only sample data (from Steps 7 and 9) should be transformed by the agent for validation purposes.

If run_cell is available: "I've added the execution cell to the notebook. Would you like me to run it?" Otherwise: "I've added the execution cell to the notebook. You can run it to transform the full dataset. Would you like to review the notebook before running it?"

⏸ Wait for user.

Step 11: Verify and confirm with the user

For this step, you need: to verify the output looks correct and confirm with the user.

Read 1–2 sample records from the output to show the user.
Report the total number of records transformed.
Ask the user if the output looks good.

⏸ Wait for user to confirm.

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

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Instructions that use the network
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Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · skills/core-skills/aws-ai-ml · commit 0a218f8

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

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aws/agent-toolkit-for-aws

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From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by AWS

AWS AI/ML Model Customization

Routing

Rules

Files

references/dataset-transformation/overview.md

Dataset Transformation Agent

When to Use

Prerequisites

Principles

Known Dataset Formats Reference

Training Data Formats

Evaluation Data Formats

Workflow

Step 1: Determine transformation purpose

Step 2: Set expectations

Step 3: Understand the dataset transformation task

Step 4: Get the dataset from the user

Step 5: Examine sample data

Step 6: Get the dataset output location

Step 7: Generate and validate the transformation function

Step 8: Determine output target

Step 9: Generate the execution code

Step 10: Determine and confirm execution mode

Step 11: Verify and confirm with the user

Requirements and compatibility

Security

Attribution and licence

Related skills

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