Machine Learning in Production: Best Practices, Pitfalls, and Real-World Examples

Introduction

Deploying machine learning models is more than just training and exporting a file. In this post, we'll cover the full lifecycle: from data collection to monitoring in production, with practical tips and real-world stories.

The ML Lifecycle

1. Data Collection & Cleaning 2. Feature Engineering 3. Model Training 4. Validation & Testing 5. Deployment 6. Monitoring & Retraining

Best Practices for ML in Production

- Use version control for data, code, and models - Automate testing and validation - Monitor model drift and performance - Build retraining pipelines - Document everything

Common Pitfalls

- Training-serving skew - Data leakage - Lack of monitoring - Ignoring edge cases - Overfitting to historical data

Case Study: Predicting User Churn

A SaaS company deployed a churn prediction model. Initial results were promising, but after 3 months, accuracy dropped. Monitoring revealed a change in user behavior post-pandemic. The team retrained the model with new data, restoring performance.

Tools and Frameworks

- MLflow for experiment tracking - Seldon Core for model serving - Prometheus & Grafana for monitoring - Airflow for pipelines

Sample Deployment Code

import joblib
from flask import Flask, request, jsonify

app = Flask(__name__)
model = joblib.load("model.pkl")

@app.route("/predict", methods=["POST"]):
def predict():
    data = request.get_json(force=True)
    prediction = model.predict([data["features"]])
    return jsonify({"prediction": prediction[0]})

if __name__ == "__main__":
    app.run()

Conclusion

Machine learning in production is a journey, not a destination. With the right practices and tools, you can deliver robust, reliable, and impactful ML solutions.