mlops-automation

name	mlops-automation
description	Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.

MLOps Automation

Goal

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

Prerequisites

• Language: Python 3.14
• Manager: uv
• Context: Preparing for scale and deployment.

Instructions

1. Task Automation

Expose a single, shared task vocabulary with mise (replaces just/make).

1. Tool: mise — pins the toolchain and defines tasks in mise.toml.
2. Vocabulary: install, format, check, test, build, watch. Run everything via mise run <task> so hooks and CI reuse the same entrypoints.
3. Core Tasks:
   • format: Format code and config (ruff format, dprint fmt).
   • check: Static checks (ruff check, ty, security rules).
   • test: Run pytest.
   • build: Build the wheel (uv build).

2. Git Hooks

Catch issues locally with lefthook (replaces pre-commit).

1. Framework: lefthook with thin hooks — every command delegates to a mise run task so hooks and CI stay identical.
2. pre-commit: Run mise run format then mise run check.
3. pre-push: Run mise run test.
4. Security: Prefer Ruff S rules (replaces bandit) plus pip-audit/gitleaks (see the Validation skill), not a separate scanner.
5. Commits: Enforce Conventional Commits (e.g., feat: add new model) so git-cliff can generate the changelog.

3. Containerization

Reproducibility anywhere.

1. Tool: docker.
2. Base Image: Use python:3.14-slim for a minimal footprint; install uv in the build stage.
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 with GitHub Actions.

1. Platform: ask for the company CI/CD platform, or use github-actions for GitHub.
2. Toolchain: Bootstrap every job with actions/checkout@v7 + jdx/mise-action@v4 so CI runs the exact same mise run tasks as local hooks.
3. Workflows:
   • ci.yml: On push/PR, run mise run format, mise run check, mise run test.
   • cd.yml: On Release, build the image and publish docs via the official GitHub Pages Actions (configure-pages, upload-pages-artifact, deploy-pages).
4. Optimization: Use concurrency to cancel redundant runs.

5. AI/ML Experiments & Registry

Manage the ML lifecycle with MLflow 3.

1. Platform: MLflow (v3).
2. Tracking:
   • Use mlflow.autolog(); log metrics, params, and artifacts.
   • The file store is deprecated — opt in for local runs with MLFLOW_ALLOW_FILE_STORE=true; use a real backend (SQL/HTTP) in production.
3. Models: Log models with the keyword name= (e.g., mlflow.pyfunc.log_model(name=...)).
4. Validation: Gate promotion with mlflow.validate_evaluation_results against explicit metric thresholds.
5. 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.

Self-Correction Checklist

• Task Vocabulary: Does mise run check (and format/test/build) work?
• Hooks: Do lefthook hooks delegate to mise run tasks?
• Image: Is the Dockerfile multi-stage on python:3.14-slim?
• CI/CD: Do ci.yml/cd.yml bootstrap with jdx/mise-action@v4?
• Tracking: Are runs logged to MLflow with validated thresholds?