Recommendation System: Candidate Retrieval
How do you narrow down 10 million items to 1000 candidates in under 50ms? The art of fast retrieval at scale.
ML System Design
Production-grade machine learning system designs covering end-to-end architecture, scalability, and real-world engineering trade-offs. Learn how to build ML systems that serve millions of users.
Each design includes:
- Functional and non-functional requirements
- High-level architecture with diagrams
- Component deep-dives and implementation details
- Scaling and optimization strategies
- Monitoring, evaluation, and failure handling
- Trade-off analysis and decision frameworks
Browse by Domain
Recommendation Systems:
Classification Systems:
Data Infrastructure:
Experimentation & Metrics:
Model Serving:
Model Evaluation:
Feature Engineering:
Model Deployment:
Model Training:
Infrastructure:
Search & Ranking:
- Coming soon…
Computer Vision:
- Coming soon…
Natural Language Processing:
- Coming soon…
Real-Time Systems:
- Coming soon…
Feature Engineering & Stores:
- Coming soon…
Model Serving & Deployment:
- Coming soon…
System Design Index
Below you’ll find all ML system design problems in chronological order:
Content created with the assistance of large language models and reviewed for technical accuracy.
Classification Pipeline Design
From raw data to production predictions: building a classification pipeline that handles millions of requests with 99.9% uptime.
Data Preprocessing Pipeline Design
How to build production-grade pipelines that clean, transform, and validate billions of data points before training.
A/B Testing Systems for ML
How to design experimentation platforms that enable rapid iteration while maintaining statistical rigor at scale.
Batch vs Real-Time Inference
How to choose between batch and real-time inference, the architectural decision that shapes your entire ML serving infrastructure.
Model Evaluation Metrics
How to measure if your ML model is actually good, choosing the right metrics is as important as building the model itself.
Feature Engineering at Scale
Feature engineering makes or breaks ML models, learn how to build scalable, production-ready feature pipelines that power real-world systems.
Model Serving Architecture
Design production-grade model serving systems that deliver predictions at scale with low latency and high reliability.
Online Learning Systems
Design systems that learn continuously from streaming data, adapting to changing patterns without full retraining.
Caching Strategies for ML Systems
Design efficient caching layers for ML systems to reduce latency, save compute costs, and improve user experience at scale.
Content Delivery Networks (CDN)
Design a global CDN for ML systems: Edge caching reduces latency from 500ms to 50ms. Critical for real-time predictions worldwide.
Distributed ML Systems
Design distributed ML systems that scale to billions of predictions: Master replication, sharding, consensus, and fault tolerance for production ML.
Resource Allocation for ML
Build production ML infrastructure that dynamically allocates resources using greedy optimization to maximize throughput and minimize costs.
Model Ensembling
Build production ensemble systems that combine multiple models using backtracking strategies to explore optimal combinations.