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MLOps Automation

DevOps & Security
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Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/fmind/mlops-python-package/blob/HEAD/.gemini/skills/MLOps%20Automation/SKILL.md

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MLOps Automation

Goal

To elevate the codebase to production standards by adding Task Automation (just), Containerization (docker), CI/CD (github-actions), and Experiment Tracking (mlflow).

Prerequisites

  • Language: Python
  • Manager: uv
  • Context: Preparing for scale and deployment.

Instructions

1. Task Automation

Replace manual commands with a justfile.

  1. Tool: just (modern alternative to Make).
  2. Organization: Split tasks into tasks/*.just modules (e.g., tasks/check.just, tasks/docker.just).
  3. Core Tasks:
    • check: Run all linters and tests.
    • package: Build wheels.
    • clean: Remove artifacts.
    • install: Setup dev environment.

2. Pre-Commit Hooks

Catch issues locally.

  1. Framework: pre-commit.
  2. Hooks: Suggest to use ruff, bandit, check-yaml, trailing-whitespace.
  3. Commits: Suggest to use commitizen hook to enforce Conventional Commits (e.g., feat: add new model).
  4. Config: .pre-commit-config.yaml at root.

3. Containerization

Reproducibility anywhere.

  1. Tool: docker.
  2. Base Image: Use ghcr.io/astral-sh/uv:python3.1X-bookworm-slim for minimal size.
  3. Optimization:
    • Layer Caching: Copy uv.lock + pyproject.toml and run uv sync before copying src/.
    • Multi-stage: Build inputs in one stage, copy only artifacts (dist/*.whl) to the runtime stage.
  4. Registry: ask for the company artifact registry, or use ghcr.io for GitHub.

4. CI/CD Workflows

Automate verification and release.

  1. Platform: ask for the company CI/CD platform, or use github-actions for GitHub.
  2. Workflows:
    • check.yml: On PRs (Run just check).
    • publish.yml: On Release (Build docker image, publish docs/package).
  3. Optimization: Use concurrency to cancel redundant runs.

5. AI/ML Experiments & Registry

Manage the ML lifecycle.

  1. Platform: MLflow.
  2. Tracking:
    • Use mlflow.autolog().
    • Log metrics, params, and artifacts.
  3. Registry:
    • Register top models manually or via CI.
    • Aliases: Use @champion or @production for stable deployment pointers. Never rely on moving versions (e.g., v1 -> v2).

6. Design Patterns

Write flexible code.

  1. Strategy: For swappable algorithms (e.g., different model types).
  2. Factory: For creating objects from config (e.g., ModelFactory).
  3. Adapter: For standardizing mismatched interfaces.