| Step | Name | Key Questions | |------|------|----------------| | | M otivation & Metrics | What business problem? Offline metrics (accuracy, F1, AUC, NDCG) → online metrics (CTR, conversion, latency, throughput) | | 2 | L eap of Faith / Simplest Baseline | What’s the simplest ML model that works? (e.g., logistic regression, k-NN, XGBoost) | | 3 | E xplore Data & Features | Data sources, labeling, feature types (continuous, categorical, text, image), feature engineering, data splits (time-based if needed) | | 4 | D esign Architecture | Model choice, training pipeline, inference (batch vs. real-time), deployment, monitoring, trade-offs |
The book by and Ali Aminian has become a definitive guide for engineers navigating the complexities of architecting large-scale machine learning (ML) solutions. It bridges the gap between theoretical ML models and the production-grade infrastructure required to support them. The Core Framework: A 7-Step Approach machine learning system design interview pdf alex xu
The book (and accompanying PDFs) provides deep dives into real-world systems. Here are the core architectures covered: 📱 Visual Search System (Pinterest Style) : Embeddings and Vector Databases. | Step | Name | Key Questions |