Introduction: The Deployment Gap
You've built an amazing machine learning model. It achieves 95% accuracy on your test set. You're excited to share it with the world... and then reality hits.
The deployment gap is real:
- Your model works great in Jupyter, but fails in production
- It takes 5 seconds to predict (users want < 100ms)
- It crashes under load
- You can't monitor or debug it
- Rolling back changes is a nightmare
Key Insight: A model that's not deployed is worthless. Production deployment is where the real engineering challenges begin!
Learning Objectives
- Understand production requirements (latency, throughput, reliability)
- Learn deployment patterns (batch vs. real-time, edge vs. cloud)
- Master model serving with REST APIs
- Implement monitoring and logging
- Handle versioning and rollback
- Scale to handle production traffic
- Optimize for different deployment environments
1. Production Requirements
The 3 Pillars of Production ML
1. Performance
- Latency: Time to return a prediction (target: < 100ms for real-time)
- Throughput: Requests handled per second (RPS)
- Resource usage: CPU, RAM, GPU
2. Reliability
- Availability: System uptime (target: 99.9% = 43 minutes downtime/month)
- Error handling: Graceful degradation
- Monitoring: Detect issues before users do
3. Maintainability
- Versioning: Track model versions
- Rollback: Quickly revert to previous version
- A/B testing: Compare models in production
- Continuous deployment: Automated updates
Interactive Exploration
FastAPI (Modern Alternative)
4. Docker Containerization
Why Docker?
Problem: "It works on my machine" 🤷♂️
Solution: Package everything (code, dependencies, environment) into a container!
Dockerfile Example
5. Monitoring and Logging
Key Metrics to Monitor
System Metrics:
- CPU/GPU utilization
- Memory usage
- Request latency (p50, p95, p99)
- Throughput (requests/second)
- Error rate
Model Metrics:
- Prediction distribution
- Input feature drift
- Output drift (are predictions changing over time?)
- Model confidence/uncertainty
Logging Best Practices
6. Model Versioning and A/B Testing
Model Versioning Strategy
A/B Testing
7. Performance Optimization
Optimization Strategies
1. Model Optimization
- Quantization (reduce precision: FP32 → FP16 or INT8)
- Pruning (remove unnecessary weights)
- Distillation (train smaller model to mimic larger one)
2. Serving Optimization
- Batch predictions together
- Use model compilation (TensorRT, ONNX Runtime)
- Cache frequently requested predictions
- Load balancing across multiple instances
3. Infrastructure Optimization
- Auto-scaling based on load
- Use appropriate hardware (CPU vs GPU vs TPU)
- Geographic distribution (CDN for models)
Benchmarking Example
Key Takeaways
✅ Production ML is fundamentally different from experimentation
✅ Performance matters: Optimize for latency, throughput, and resource usage
✅ Reliability: Monitor, log, and handle errors gracefully
✅ Deployment patterns: Choose real-time vs batch, cloud vs edge based on requirements
✅ Containerization: Use Docker for consistent, reproducible deployments
✅ Versioning: Track models, enable rollback, and A/B test new versions
✅ Monitoring: Measure system AND model metrics continuously
What's Next?
Next lesson: MLOps Fundamentals – automating the ML lifecycle with CI/CD, orchestration, and production best practices!