Building Scalable ML Systems

Deploying machine learning models to production requires careful consideration of scalability, reliability, and maintainability.

Architecture Principles

Separation of Concerns

Keep data processing, model training, and inference as separate, loosely coupled services.

Horizontal Scaling

Design your system to scale horizontally by adding more instances rather than vertically scaling single machines.

Model Serving

Choose the right serving strategy:

• Batch prediction: Process large volumes of data periodically
• Real-time inference: Low-latency predictions for individual requests
• Streaming: Process continuous data streams

Monitoring and Observability

Track key metrics:

• Model performance (accuracy, latency, throughput)
• Data drift detection
• System health (CPU, memory, network)
• Business metrics

Conclusion

Building scalable ML systems is as much about software engineering as it is about data science. Focus on clean architecture, automated testing, and continuous monitoring.