: A highly structured repository that breaks down classic ML design questions into modular components.
: What is the scale of the system (users, items)? Are there latency constraints (e.g., predictions under 50ms)? Is this an online (real-time) or offline (batch) system? 2. Define Metrics (Business vs. ML)
Design a movie recommendation engine (e.g., Netflix) or a short-form video feed algorithm (e.g., TikTok).
: Data Lakes (S3) for raw data, Data Warehouses (Snowflake) for structured features, Feature Stores (Feast) for low-latency serving. 4. Engineering Features Types : Categorical, numerical, text, embeddings. Handling Missing Data : Imputation vs. removal.
: Unlike resources that focus only on algorithms, it covers data pipelines, serving infrastructure, and monitoring. Pros and Cons Interview-Oriented Machine Learning System Design Interview Pdf Github
: Handling class imbalance via downsampling the majority class or upsampling (SMOTE). 7. Deployment and Serving Infrastructure
A highly structured repository dedicated exclusively to cracking the interview at FAANG companies. It includes architectural diagrams, cheat sheets, and deep dives into the trade-offs between model accuracy and system latency. 3. Alimustafa / Machine-Learning-System-Design
If you are preparing for a senior role at a top tech company (FAANG or similar), you have likely realized something unsettling: The real differentiator—and the reason many candidates fail—is the Machine Learning System Design interview.
Do you need real-time predictions under 50ms, or is offline batch processing acceptable? 2. Data Engineering & Pipeline Design : A highly structured repository that breaks down
Identify user profiles, historical logs, or real-time context.
While not a direct PDF, this repo indexes the best video breakdowns of ML systems. Videos are better than PDFs for understanding the motion of data through a pipeline.
: Extreme class imbalance, adversarial attackers continuously changing tactics, and zero-tolerance for high latency.
: An excellent blueprint that focuses heavily on production issues, detailing model monitoring, data quality, and observability strategies. Highly Recommended PDFs and Books Is this an online (real-time) or offline (batch) system
A system is not designed until it is successfully deployed and monitored in production.
This repository is highly regarded for its structured approach to ML system design. It contains step-by-step breakdown templates that you can apply to any interview question, helping you stay organized under pressure. chiphuyen/machine-learning-systems-design
Start with a simple baseline (e.g., Logistic Regression or a simple tree-based model) before moving to complex deep learning architectures.
