Deploying AI Models to Production: A Complete Guide

# Track key metrics
import time
import logging

def predict_with_monitoring(input_data):
    start = time.time()
    result = model.predict(input_data)
    latency = time.time() - start
    
    logging.info(f"Prediction: {result}, Latency: {latency:.3f}s")
    
    # Check for data drift
    check_drift(input_data)
    
    return result

Step 6: Scaling

• Horizontal scaling: Add more instances behind a load balancer
• Model quantization: Reduce model size for faster inference
• Batching: Process multiple requests together
• Caching: Cache results for common inputs

Cost Optimization

• Use spot instances for batch processing
• Cache frequently requested predictions
• Quantize models to reduce GPU memory
• Use model distillation for simpler tasks

Real Example

A Moroccan fintech deployed an AI fraud detection model:

• Containerized with Docker
• Deployed on AWS ECS with auto-scaling
• Processes 10,000+ transactions per minute
• 99.9% uptime with multi-AZ deployment
• Under 100ms latency per prediction

Next Steps

Our "Build with LLMs" programme teaches production deployment of AI applications.