Blog Post··4 min read
ML System Design: Applied Systems
Five applied systems — content moderation, ETA, visual search, document extraction, multimodal search — each reduced to its one interesting design decision.
Tag: ml-system-design
Blog Post··4 min read
ML System Design: A Framework That Actually Works
The universal seven-step framework for any ML system design problem — and the specific mistakes that make interviewers fail strong candidates.
Blog Post··4 min read
ML System Design: Infrastructure
Feature stores (online/offline duality), data vs model parallelism for distributed training, and why A/B testing ML models is harder than product A/B tests.
Blog Post··4 min read
ML System Design: LLM Systems
Serving LLMs (KV cache, continuous batching, speculative decoding), building enterprise RAG (chunking, hybrid retrieval, reranking), and the fine-tuning pipeline.
Blog Post··3 min read
ML System Design: The 20-Question Practice Set
All 20 canonical ML system design questions as an interview prep set — what makes each hard, the anchoring design decision, and what a strong answer includes. Prompts, not solutions.
Blog Post··4 min read
ML System Design: Real-Time ML
Fraud, anomaly detection, and real-time bidding share three enemies: a tight latency SLA, concept drift, and extreme class imbalance. How production systems handle all three at once.
Blog Post··5 min read
ML System Design: Recommendation Systems
Candidate generation, ranking, and reranking — the three-stage funnel behind YouTube and Spotify. Two-tower retrieval, why the funnel exists, and how cold start is actually solved.
Blog Post··4 min read
ML System Design: Search & Feed Ranking
Learning to rank — pointwise, pairwise, listwise — plus query understanding with BERT and what feed ranking adds: engagement prediction and diversity.