Machine Learning System Design Interview Alex Xu Pdf __top__ -

What data is available immediately? Is it labeled? Are there privacy or compliance restrictions?

Choose between Online Inference (low latency, computed on the fly using a model server like Triton) and Batch Inference (pre-computed predictions stored in a NoSQL database for rapid lookup).

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Ask about the scale. How many Daily Active Users (DAU) will the system support? What is the acceptable inference latency (e.g., under 50ms)?

Draw a clear line between the offline phase (training) and the online phase (serving). Your high-level architecture diagram should visually separate these two workflows. What data is available immediately

Propose a unified Feature Store (like Feast). This ensures that both offline training and online serving use the exact same feature definitions, preventing offline-online data leakage. 3. Deep Dive into ML Specifics

3. Real-World Case Study: Designing a Feed Recommendation System Choose between Online Inference (low latency, computed on

However, beware of the . Reading a PDF about building a recommender system is not the same as explaining, under time pressure, why your embedding layer is too large for the memory budget.

: Online feature extraction, negative downsampling, and streaming data pipelines (like Kafka and Flink) to capture real-time user intent. 2. Design a Video/Content Recommendation System