6 min read Search ranking is one of the most technically demanding ML system design problems. It combines information retrieval, multi-stage ranking, real-time […] Read article
6 min read NLP interview questions appear across ML engineer, data scientist, and applied researcher roles at companies like Google, Meta, OpenAI, and Read article
6 min read Model drift is one of the most common production ML failure modes — and one of the most underestimated in Read article
4 min read “Design a spam classifier” is one of the most common ML system design questions at Google, Meta, and Microsoft. Unlike Read article
5 min read Fraud detection is one of the highest-stakes ML applications — a false negative costs money, a false positive costs a Read article
5 min read Design a real-time collaborative document editor like Google Docs. This is one of the most technically nuanced system design problems Read article
5 min read Design a recommendation engine like Netflix’s, Spotify’s Discover Weekly, or Amazon’s “Customers also bought.” Recommendation systems are one of the Read article
6 min read Design an LLM inference API — the service that accepts user prompts and returns model completions, like the OpenAI API, Read article
5 min read RLHF (Reinforcement Learning from Human Feedback) is the technique that transforms a raw language model into an assistant — the Read article
6 min read Computer vision is one of the most interview-tested areas of ML, especially at companies with physical products, autonomous systems, or Read article
6 min read Design a monitoring and alerting system like Datadog, Prometheus + Grafana, or New Relic. This is a system design problem Read article
5 min read Design an ad click aggregation system — the infrastructure that counts how many times each ad was clicked, detects fraud, Read article
5 min read Retrieval-Augmented Generation (RAG) is one of the most widely deployed LLM patterns in production. Understanding when to use RAG versus Read article
6 min read Embeddings are the lingua franca of modern AI applications. They power semantic search, RAG, recommendation systems, duplicate detection, and anomaly Read article
6 min read Transformers are the architecture behind GPT, BERT, Claude, and every other major language model. Understanding how they work — especially Read article
