MLOps Automation

Task automation, containerization, CI/CD, and experiment tracking

Automate tasks, containers, CI/CD, and ML experiments.

Features

1. Task Runner (just) ⚡

Copy justfile:

bash

cp references/justfile ../your-project/

Tasks:

• just check - Run all checks
• just test - Run tests
• just build - Build package
• just clean - Remove artifacts
• just train - Run training

2. Docker 🐳

Multi-stage build:

bash

cp references/Dockerfile ../your-project/
docker build -t my-model .
docker run my-model

Optimizations:

• Layer caching (uv sync before copy src/)
• Minimal runtime image
• Non-root user

3. CI/CD (GitHub Actions) 🔄

Automated pipeline:

bash

cp references/ci-workflow.yml ../your-project/.github/workflows/ci.yml

Runs on push/PR:

• Lint (Ruff + MyPy)
• Test (pytest + coverage)
• Build (package + Docker)

Quick Start

bash

# Setup task runner
cp references/justfile ./

# Setup CI
mkdir -p .github/workflows
cp references/ci-workflow.yml .github/workflows/ci.yml

# Setup Docker
cp references/Dockerfile ./

# Test locally
just check
docker build -t test .

MLflow Tracking

python

import mlflow

mlflow.autolog()
with mlflow.start_run():
    mlflow.log_param("lr", 0.001)
    model.fit(X, y)
    mlflow.log_metric("accuracy", acc)

Author

Converted from MLOps Coding Course

Changelog

v1.0.0 (2026-02-18)

• Initial OpenClaw conversion
• Added justfile template
• Added Dockerfile
• Added CI workflow