Real ML Systems

Most ML content focuses on models and algorithms. Real production ML systems are dominated by everything around the model — data pipelines, monitoring, feature stores, and operational complexity.

Key Takeaways

• Data quality matters more than model architecture
• Monitoring and observability are non-negotiable in production
• Feature engineering pipelines are often the most complex part
• Model serving infrastructure requires careful capacity planning
• Feedback loops between production data and retraining are critical

The actual model code is often a small fraction of the total system.