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Design a Computer Vision System

Build an end-to-end computer vision system for image classification, object detection, and real-time video processing at scale with deep learning models.

ML SystemsComputer VisionReal-time ML
Q: What's the expected scale and types of visual content processing?
A: Process 1M+ images daily with 100+ concurrent video streams. Support image classification, object detection, face recognition, OCR, and visual search across 4K resolution content.
Engineering Implications: Scale drives architecture: need distributed GPU clusters, stream partitioning, and efficient model serving. Different CV tasks require specialized models and processing pipelines with varying resource requirements.
Q: What are the latency and accuracy requirements?
A: Sub-100ms inference latency for real-time applications, 95%+ accuracy on benchmark datasets, 99.9% API availability. Support both batch and real-time processing modes.
Engineering Implications: Sub-100ms latency requires GPU acceleration, model optimization, and efficient preprocessing. High accuracy demands ensemble methods and continuous model monitoring for drift detection.
Q: How should the system handle different model types and deployment?
A: Support multi-model ensemble serving with 50+ different architectures, model versioning, A/B testing, and edge-cloud hybrid deployment for optimal performance.
Engineering Implications: Multi-model serving requires dynamic resource allocation, intelligent routing, and heterogeneous deployment strategies. Edge deployment needs model compression while cloud handles complex tasks.
Q: What compliance and privacy requirements exist?
A: GDPR-compliant data handling for biometric data, on-device processing for sensitive content, secure feature storage, consent management, and audit trails for regulatory compliance.
Engineering Implications: Privacy-first architecture: process sensitive data locally when possible, encrypt features at rest, implement data retention policies, and provide user control over biometric data processing.
Q: How should the system handle model training and updates?
A: Continuous training on 100M+ images with daily model updates, distributed training across GPU clusters, hyperparameter optimization, and automated deployment pipelines.
Engineering Implications: Production ML requires robust training infrastructure, data versioning, model validation pipelines, and safe deployment strategies. Need to handle data drift and model degradation proactively.
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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of computer vision systems in interviews.

1. Real-time Object Detection Pipeline

"Your system processes 1000+ concurrent video streams for real-time object detection. How do you handle variable frame rates, ensure temporal consistency across frames, and manage GPU memory when some streams are 4K while others are 720p?"

2. Model Ensemble Strategy

"You need to combine predictions from YOLOv8 (fast, 85% accuracy), Vision Transformer (slow, 95% accuracy), and a custom domain-specific model. How do you design an ensemble system that maximizes accuracy while maintaining sub-100ms latency for 90% of requests?"

3. Privacy-Preserving Computer Vision

"Design a face recognition system for security cameras that processes biometric data. How do you ensure GDPR compliance, implement on-device processing for sensitive areas, and handle consent management while maintaining 99.5% accuracy?"

4. Edge-Cloud Hybrid Architecture

"Your CV system serves autonomous vehicles requiring <10ms latency for safety-critical decisions. How do you architect an edge-cloud hybrid system that processes basic detection locally but leverages cloud for complex scene understanding and model updates?"

5. Dynamic Model Optimization

"During peak hours, your GPU cluster reaches 95% utilization causing latency spikes. How do you implement dynamic model optimization that automatically switches between full-precision and quantized models based on current load while monitoring accuracy degradation?"

6. Continuous Learning Pipeline

"Your image classification model degrades from 95% to 87% accuracy over 6 months due to distribution drift. Design a continuous learning system that automatically detects drift, acquires new training data, retrains models, and deploys updates while preventing catastrophic forgetting."