This one took me a second to figure out why Netflix would even ask it, which probably cost me some composure early on.
Start by clarifying requirements and scale (e.g., volume of images, latency, accuracy, privacy regulations). Then propose a scalable pipeline with stages for ingestion, detection, blurring, and storage, discussing trade-offs between accuracy, cost, and latency. Emphasize modularity, monitoring, and iterative improvement.
Pro tip: Highlight the importance of human-in-the-loop for edge cases and continuous model retraining to adapt to new scenarios, showing you understand real-world deployment challenges beyond just the ML model.
Ask about scale (images per day), latency requirements, accuracy targets, types of sensitive information, and regulatory compliance (e.g., GDPR). This ensures the design meets actual needs.
Outline a pipeline: ingestion (from street-level imagery sources), preprocessing, detection (using ML models), blurring, and storage/delivery. Consider batch vs. real-time processing.
Discuss model choices (e.g., YOLO, Faster R-CNN for detection; GANs for blurring), trade-offs between accuracy and speed, and techniques like ensemble models or cascading to improve precision.
Explain how to scale horizontally using distributed processing (e.g., Spark, Kubernetes), handle failures with retries and dead-letter queues, and ensure low latency via caching or edge processing.
Describe metrics (precision, recall, blur quality), A/B testing, and feedback loops for model retraining. Include human review for ambiguous cases and compliance auditing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.