Deploying and Monitoring Machine Learning Models

Intermediate

🚀 Model Deployment & Maintenance in Machine Learning

🧩 Deployment

Deployment involves integrating the trained model into a production environment to make:

  • ⚡ Real-time predictions
  • 🕒 Batch predictions

Common deployment platforms:

  • ☁️ Cloud services
  • 🌐 APIs
  • 🧱 Embedded systems

📈 Post-Deployment Monitoring

Continuous monitoring ensures the model maintains performance over time.
Key indicators include:

  • 🎯 Prediction accuracy
  • 🕰️ Latency
  • 🔄 Data drift

🔁 Model Maintenance

To prevent performance degradation, models are periodically retrained with new data.

📊 Tools:

  • 📉 Visual dashboards
  • 🚨 Alert systems
  • 🔄 Automated retraining pipelines

🛡️ Example: Fraud Detection in Banking

A fraud detection system must continuously adapt as fraud patterns evolve —
just like updating a security guard’s knowledge base to stay alert against new threats. 🔍