ML Systems Architecture 101: What Every Engineer Should Know

ML systems architecture is the study of how production ML actually works at the engineering level: how data flows through feature pipelines, how models are trained and versioned, how predictions are served, and how systems are monitored for degradation. It is the foundation beneath every production model — yet many ML practitioners have only a superficial understanding of its key concepts.

The first essential concept is the training-serving boundary. Training produces a model artifact; serving loads that artifact and produces predictions. The two environments have fundamentally different requirements: training optimizes for throughput (process as many examples as possible per second), while serving optimizes for latency (produce a prediction in as few milliseconds as possible). Understanding this boundary is critical because decisions made during training — batch size, feature preprocessing, numerical precision — directly affect serving performance.

The second concept is feature consistency. When a model is trained on features computed one way and served features computed a different way, the result is training-serving skew — a silent killer of model accuracy. The solution is to compute features with the same code in both contexts, typically through a feature store that materializes features for both batch training and real-time serving.

The third concept is model monitoring and feedback loops. A deployed model's performance degrades over time as input distributions shift. Monitoring must track not just system metrics (latency, throughput, error rate) but also model-specific metrics (prediction confidence, feature drift, label distribution). When degradation is detected, the system should trigger retraining, A/B testing of the new model, and gradual rollout — a feedback loop that keeps the model current without risking production stability.