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ML System Design — A Complete Guide (2026)

A comprehensive guide to designing production ML systems — from data pipelines and training to serving, monitoring, and interview prep.

Table of Contents

Part I: Foundations & Architecture

• 1. Introduction What is ML system design, importance, comparison with traditional systems
• 2. Core Objectives Scalability, latency, reliability, adaptability, explainability/fairness
• 3. Stages of ML Systems Data pipeline, training, serving
• 4. Step-by-Step Architecture Data ingestion, storage, preprocessing, training, deployment, serving, monitoring
• 5. Core Components Feature store, model registry, inference API, feedback loop
• 6. Batch vs Real-Time Systems Lambda/Kappa architectures, trade-offs

Part II: Training & Serving

• 7. Training Architecture Data parallelism, model parallelism, parameter servers, checkpointing
• 8. Serving Architecture Load balancer, inference API, caches, model servers, logging
• 9. Caching Strategies Feature, inference, model cache
• 10. Indexing Vector, inverted, hash indexing
• 11. Scalability Horizontal scaling, partitioning, queues, load balancing, auto-scaling
• 12. Fault Tolerance Replication, retries, fallback models, monitoring

Part III: Operations & Interview Prep

• 13. Monitoring & Drift Detection Accuracy metrics, latency, throughput, drift detection
• 14. Security & Privacy Encryption, RBAC, differential privacy, bias/fairness, compliance
• 15. Trade-Offs Accuracy vs latency, freshness vs stability, cost vs redundancy, consistency vs availability
• 16. Case Study Recommendation system with retrieval + ranking + caching
• 17. Interview Prep Roadmap Clarify, estimate scale, outline architecture, deep dive, trade-offs, reliability/ethics, improvements
• 18. FAQ Common questions about ML system design
• 19. Wrap-Up Key takeaways, resources, mindset