This repository acts as a highly organized directory of core ML design principles. It breaks down complex case studies like Facebook’s News Feed ranking and Uber’s Michelangelo platform.

Using the resources above, you'll develop a structured approach to any design problem. A typical framework you will learn includes:

Define a simple, rule-based baseline to prove an ML model is actually necessary (e.g., recommend the most popular items globally first). 3. Data Engineering & Feature Pipeline

If you want to learn more about machine learning system design, you can take online courses:

"Machine Learning System Design Interview" by Ali Aminian & Alex Xu

Raw data storage (Data Lake/S3) vs. structured data warehouses (BigQuery/Snowflake).

What is the ultimate objective? (e.g., increase user watch time, maximize ad revenue).

Avoid data leakage by using time-based splits rather than random splits for time-sensitive data.

Enables Approximate Nearest Neighbor (ANN) search for massive embedding catalogs in under 10ms. Triton Server, TorchServe, TF Serving

Use Canary deployments or Shadow deployments to test the model on a small percentage of live traffic.

If you are looking for downloadable cheat sheets, structured PDFs, and comprehensive reading lists, these open-source GitHub repositories are essential. 1. Khangwong / machine-learning-system-design

Detail how the model learns and how you validate its performance before production.

An ML system is only as good as its data. Explain how data flows into your model.

Identify latency requirements (e.g., inference under 50ms), throughput, and data privacy limits. 2. Data Engineering & Pipeline Design

In a machine learning system design interview, you'll be asked to design and architect a machine learning system to solve a specific problem. The interviewer will assess your ability to: