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/aws-ai-ml

@7fcb1da

Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing/selecting which base model to customize or fine-tune from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, benchmarking or optimizing inference, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

Use this Skill: https://skilld.dev/gh/aws/agent-toolkit-for-aws/aws-ai-ml

This session only. Nothing lands on disk.

referencesfinetuning-techniqueoverview.md

≈547 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Finetuning Technique

Guides the user through selecting a fine-tuning technique based on their use case and validates compatibility with the selected model.

When to Use

  • User has decided to finetune and needs to choose a technique
  • User wants to change their finetuning technique
  • Technique needs to be validated against a selected model

Prerequisites

  • A base model has been selected (via model-selection reference). The model name and hub must be known.
  • A use_case_spec.md file exists. If not, load the use-case-specification reference to generate it first.

Workflow

Step 1: Determine Finetuning Technique

Consult references/finetune_technique_selection_guide.md to recommend the best-fit technique based on the use case and the user's needs (SFT, DPO, RLVR, RLAIF).

Present the recommendation and reasoning to the user. Ask if they'd like to go with the recommendation or prefer a different technique.

Step 2: Validate Technique Availability

  1. Once the user confirms a technique, retrieve the finetuning techniques available for the selected model by running: python finetuning-technique/scripts/get_recipes.py <model-name> <hub-name>
    • This script filters to SFT, DPO, RLVR, and RLAIF, which have validated workflows in this skill. The model may support additional techniques (e.g. CPT, MTRL, PPO) that are not returned by this script.
  2. If the chosen technique is available for the model, proceed to Step 3.
  3. If the chosen technique is not available for the model, explain that the selected model does not support it on SageMaker and offer to go back to model-selection to pick a different model that supports the chosen technique. If the technique is one the model may support but is not returned by this script (e.g. CPT, MTRL, PPO), explain that this skill does not have a validated workflow for it and offer to help using general knowledge.

Step 3: Confirm Selections

Present a summary to the user:

Here's what we've selected:
- Base model: [model name]
- Fine-tuning technique: [SFT/DPO/RLVR/RLAIF]

References

  • references/finetune_technique_selection_guide.md — Technique guidance (SFT/DPO/RLVR/RLAIF)

Source: SKILL.md on GitHub

1 warning16d3 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill includes some security considerations such as the ingestion of external data for model training and the execution of generated scripts. While these warrant review, they are used within the skill's intended functionality for AI/ML model customization and deployment on Amazon SageMaker. See detailed analysis for context.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: MEDIUM · 2 issues

Signed by skilld at 7fcb1da. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 weeks ago
metadata
{
  "version": "4"
}

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