🚀 MLOps Class Activity: CI/CD Pipeline using GitHub Actions

This repository is part of the MLOps course activity on Continuous Integration and Deployment (CI/CD) for Machine Learning.
The goal is to automate an ML workflow using GitHub Actions — from preprocessing to training, evaluation, and containerization.

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🎯 Learning Objectives

By the end of this activity, you will be able to:

• Understand CI/CD concepts applied to ML projects
• Automate data preprocessing, model training, and evaluation using GitHub Actions
• Upload trained model artifacts automatically
• (Optionally) Build and push Docker containers after successful training

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🧩 Repository Structure

mlops-ci-activity/
│
├── data/                      # Optional: sample dataset (if used)
│
├── preprocess.py              # Preprocessing script
├── train.py                   # Model training script
├── evaluate.py                # Model evaluation script
├── requirements.txt           # Dependencies
├── Dockerfile.train           # Docker image for training
├── Dockerfile.serve           # Docker image for serving
│
└── .github/
    └── workflows/
        └── ci-pipeline.yml    # GitHub Actions workflow definition

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⚙️ Step-by-Step Instructions

1️⃣ Fork this Repository → 2️⃣ Add Secrets (optional) → 3️⃣ Push to main → 4️⃣ Observe Actions tab
Artifacts and Docker builds will appear automatically.

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📚 References

• GitHub Actions Documentation
• MLOps with GitHub Actions
• Continuous Delivery for Machine Learning (CD4ML)