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 (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.