Machine Learning System Design Blueprint

When Netflix recommends your next binge-watch or Uber predicts your arrival time, you’re experiencing production ML systems handling millions of predictions per second. But here’s what most tutorials won’t tell you: the model is just 5% of the system. The real challenge? Building the infrastructure that keeps it alive.

The Training-Serving Skew Nobody Warns You About

Your model trained beautifully on last month’s data, achieving 95% accuracy. You deploy it to production, and within days, performance nosedives. Welcome to training-serving skew—the silent killer of ML systems. This happens when your training data diverges from production data, not because your model is bad, but because your feature engineering pipelines are inconsistent.