+-------------------------------------------------------------+ | 1. Clarify Requirements & Scope (Business Goals & Metrics) | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | 2. Data Engineering & Pipeline (Ingestion, Features, Labels)| +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | 3. Model Architecture (Selection, Training, Loss Functions) | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | 4. Deployment & Scaling (Serving, Latency, Infrastructure) | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | 5. Monitoring & Maintenance (Drift, Feedback Loops, Retrain)| +-------------------------------------------------------------+ 1. Clarify Requirements and Scope
Data is the foundation of any ML system. Explain how you will collect, store, and process this data.
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A Deep & Cross Network (DCN) is chosen to explicitly learn bounded-degree feature interactions (e.g., User Device
Instead of jumping straight into model selection, the book teaches a four-step approach: Clarify Requirements and Scope Data is the foundation
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Choose between periodic batch retraining or continuous online learning. Core Case Studies to Master self-driving car routing
What (e.g., search engine, self-driving car routing, fraud detection) do you find most challenging?
Cracking the Machine Learning System Design Interview: The Ultimate Preparation Guide
Read Designing Machine Learning Systems by Chip Huyen and Machine Learning System Design Interview by Alex Xu & Ali Aminian.