Machine Learning System Design Cheat Sheet

Updated 2026-05-18

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Machine learning system design is the architectural discipline of building end-to-end ML systems that operate reliably at scale in production. Unlike traditional software systems, ML systems must handle probabilistic outputs, continuous data evolution, and the unique challenge of serving predictions while simultaneously learning from new data. Modern ML system design integrates data pipelines, training infrastructure, model serving, experimentation frameworks, and monitoring systems into a cohesive architecture. The most critical distinction: ML systems degrade silently — without proper monitoring and retraining triggers, model performance erodes invisibly as the world changes beneath them.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 149 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core ML System Design PatternsTable 2: Feature Engineering InfrastructureTable 3: Model Training Pipeline ArchitectureTable 4: Model Registry and VersioningTable 5: Model Serving InfrastructureTable 6: Serving Performance OptimizationTable 7: Experimentation and Online EvaluationTable 8: Model Monitoring and ObservabilityTable 9: Drift Detection TechniquesTable 10: Retraining Strategies and TriggersTable 11: Data Pipeline ArchitectureTable 12: Distributed Training StrategiesTable 13: Inference Patterns and Trade-offsTable 14: ML System Security and PrivacyTable 15: Cost Optimization Strategies

Table 1: Core ML System Design Patterns

Production ML systems follow repeatable architectural patterns that balance prediction accuracy, latency, cost, and maintainability. These patterns represent proven approaches for deploying models at scale.

Pattern	Example	Description
Batch Prediction Pipeline	`predictions = model.predict(daily_data)` `store_to_cache(predictions)`	• Precomputes predictions for all inputs on a schedule (hourly/daily) • serves results from cache for low-latency lookups at the cost of staleness
Real-Time Inference Service	`@app.post("/predict")` `return model.predict(request.features)`	• Computes predictions on-demand per request • sub-100ms latency requirement drives optimization choices like model size and caching
Online Learning System	`model.partial_fit(new_batch)` `if drift_detected: retrain()`	• Updates model continuously with streaming data • trades training stability for adaptiveness to distribution shifts in real-time
Feature Store Architecture	`features = store.get_online(user_id)` `offline = store.get_historical(timestamp)`	• Centralized feature computation and serving layer • ensures training-serving consistency by using identical feature logic in both paths
Model Registry Pattern	`registry.log_model(model, metrics)` `prod_model = registry.load("v2.3")`	• Version control for trained models with lineage tracking • enables rollbacks, A/B testing, and audit trails of what ran when

Table 1: Core ML System Design Patterns

Pattern	Example	Description
Batch Prediction Pipeline	`predictions = model.predict(daily_data)` `store_to_cache(predictions)`	• Precomputes predictions for all inputs on a schedule (hourly/daily) • serves results from cache for low-latency lookups at the cost of staleness
Real-Time Inference Service	`@app.post("/predict")` `return model.predict(request.features)`	• Computes predictions on-demand per request • sub-100ms latency requirement drives optimization choices like model size and caching
Online Learning System	`model.partial_fit(new_batch)` `if drift_detected: retrain()`	• Updates model continuously with streaming data • trades training stability for adaptiveness to distribution shifts in real-time
Feature Store Architecture	`features = store.get_online(user_id)` `offline = store.get_historical(timestamp)`	• Centralized feature computation and serving layer • ensures training-serving consistency by using identical feature logic in both paths
Model Registry Pattern	`registry.log_model(model, metrics)` `prod_model = registry.load("v2.3")`	• Version control for trained models with lineage tracking • enables rollbacks, A/B testing, and audit trails of what ran when