Hands-On Project

Build Your Complete MLOps Pipeline

This capstone project ties together everything from this guide. Build it, push to GitHub, and put it on your resume.

Step 1: Project Setup

mkdir mlops-churn && cd mlops-churn
mkdir -p src/{data,features,training,serving,monitoring}
mkdir -p configs data tests reports

python -m venv venv && source venv/bin/activate
pip install pandas numpy scikit-learn mlflow feast evidently bentoml

git init && dvc init
mlflow server --backend-store-uri sqlite:///mlflow.db &

Step 2: Build Each Component

Each chapter becomes a module in your project:

# src/pipeline.py
from src.data.ingestion import DataIngestion
from src.data.validation import DataValidator
from src.features.engineering import FeatureEngineer
from src.training.trainer import ModelTrainer

def run_pipeline():
    print("== Step 1: Ingestion ==")
    data = DataIngestion().load()
    
    print("== Step 2: Validation ==")
    assert DataValidator().validate(data)
    
    print("== Step 3: Features ==")
    features = FeatureEngineer().transform(data)
    
    print("== Step 4: Training ==")
    model, metrics = ModelTrainer().train(features)
    
    print("== Step 5: Registry ==")
    ModelRegistry().register_and_promote(model, metrics)
    
    print("Pipeline complete!")

if __name__ == "__main__":
    run_pipeline()

Step 3: Dockerize

# docker-compose.yml
version: '3.8'
services:
  mlflow:
    image: python:3.10-slim
    command: bash -c "pip install mlflow && mlflow server --host 0.0.0.0"
    ports: ["5000:5000"]
  
  model-server:
    build: ./src/serving
    ports: ["3000:3000"]
    depends_on: [mlflow]
  
  monitoring:
    build: ./src/monitoring
    ports: ["8050:8050"]

Step 4: Ship It

git add .
git commit -m "feat: complete MLOps pipeline"
git push origin main

What Next?