Discuss techniques like quantization, pruning, or distillation to make models lighter and faster for production edge devices or servers. 7. Monitoring, Maintenance, and Continuous Learning
Choose between online inference (real-time predictions) or offline inference (pre-computed batch predictions cached in a NoSQL database).
For complex tasks like recommendations, map out multi-stage pipelines (e.g., Candidate Generation followed by a heavy Ranking stage). 3. Data Engineering and Feature Selection
Detail specific strategies for handling missing values (e.g., imputation or default tokens) and removing or capping anomalous data points. machine learning system design interview book pdf exclusive
What is your ? (e.g., FAANG, AI startup, Senior vs. Staff Engineer)
Do not jump straight to complex transformer architectures. Always propose a simple, robust baseline (e.g., Logistic Regression or a simple heuristic) first. Explain that this establishes a performance floor and uncovers data pipeline issues early.
Hi [Name],
Which (e.g., Search, Ad-Click Prediction, Large Language Model/Generative AI systems) do you want to break down next?
It moves beyond academic ML into real engineering—handling millions of queries, data drift, and offline/online training loops.
: Differentiate between batch processing (historical data via Spark/Hadoop) and real-time streaming (Kafka/Flink). For complex tasks like recommendations, map out multi-stage
Detail how features are managed at scale:
Which you want to deep dive into next (e.g., search engines, fraud detection, autonomous driving pipelines)?
ML systems degrade over time. A senior engineer anticipates post-deployment challenges. What is your