ML Engineering
ML System Design — Building Production ML Systems at Scale
Master the architecture and design patterns for building robust, scalable machine learning systems in production.
- Feature Stores — Centralized feature management for consistency
- Model Serving — Real-time and batch prediction architectures
- Monitoring and Observability — Ensuring models perform well in production
"A model is only as good as the system that serves it."
ML System Design — Complete Guide
ML system design combines software engineering with ML to build reliable, scalable production systems.
ML System Architecture
Real-Time vs Batch Serving
DfReal-Time Serving
Real-time serving provides sub-100ms latency for request-response patterns. Used for recommendations, fraud detection, and applications requiring immediate predictions.
DfBatch Prediction
Batch prediction processes millions of records on a schedule. Used for report generation, email campaigns, and offline processing where latency is not critical.
Key Takeaways
Summary: ML System Design
- ML systems require 4 layers: data, training, serving, monitoring
- Feature stores ensure training-serving consistency (Feast, Tecton)
- Real-time serving needs sub-100ms latency (TF Serving, Triton)
- Batch prediction for offline processing at scale (Spark, Airflow)
- Model registries version and track models (MLflow)
- Monitoring detects data drift and performance degradation
- A/B testing validates model updates before full rollout
- Scalability requires Kubernetes, autoscaling, and proper infrastructure
What to Learn Next
-> MLOps — Machine Learning Operations Complete Guide Learn about mlops — machine learning operations complete guide.
-> Model Deployment — APIs, Containers and Production ML Learn about model deployment — apis, containers and production ml.
-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.
-> Feature Stores — Managing ML Features at Scale Learn about feature stores — managing ml features at scale.
-> Capstone Projects — End-to-End ML Applications Learn about capstone projects — end-to-end ml applications.
-> Model Deployment — APIs, Containers and Production ML Learn about model deployment — apis, containers and production ml.