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Design an Image Captioning System

Build a computer vision system that automatically generates descriptive captions for 100M+ images daily with <500ms latency and multi-language support.

Vision-LanguageAccessibilityMulti-modal AI
Q: What's the expected scale and usage patterns for image captioning?
A: 100M images daily across multiple use cases: accessibility (40%), social media (35%), content management (25%). Peak load 5K QPS during US/EU hours.
Engineering Implications: Massive scale requires different optimization strategies per use case. Accessibility needs accuracy and WCAG compliance. Social media needs engaging captions. Content management needs structured metadata. Multi-region deployment essential for global latency.
Q: What types of images and visual content must be supported?
A: Photos, artwork, screenshots, charts/graphs, memes, medical images, satellite imagery. Support resolution from 224x224 to 4K+, various formats (JPEG, PNG, WebP, HEIC).
Engineering Implications: Domain diversity requires specialized model architectures. General photos need scene understanding. Charts need OCR + data interpretation. Medical images need domain-specific vocabulary. Each domain has different accuracy requirements and ethical considerations.
Q: What are the quality requirements for different use cases?
A: Accessibility: 95%+ accuracy, factual descriptions. Social media: 85%+ accuracy, engaging tone. Content management: 90%+ accuracy, structured tags.
Engineering Implications: Different quality metrics per domain: accessibility uses human evaluation for factualness, social media uses engagement metrics, content management uses precision/recall for tags. Need multi-objective training and evaluation frameworks.
Q: What latency and deployment constraints do we have?
A: Real-time applications need {'<'}500ms end-to-end, batch processing acceptable for content libraries. On-device capability for mobile apps, cloud API for web services.
Engineering Implications: Latency drives architecture decisions: real-time needs model quantization and edge deployment. Batch allows larger, more accurate models. On-device requires {'<'}100MB models. Privacy-sensitive use cases need local processing.
Q: What are the internationalization and ethical requirements?
A: Support 25+ languages including non-Latin scripts. Cultural sensitivity in descriptions. Bias mitigation for protected groups. Content safety filters.
Engineering Implications: Multilingual vision-language models with cultural context understanding. Need diverse training data and bias auditing. Different cultural norms for appropriate descriptions. Regulatory compliance (EU AI Act, accessibility laws).

Complete Image Captioning ML Systems Framework

🏗️ Section 8: Production ML System Design

  • • Multi-pipeline architecture: Real-time (30%) for <500ms, Batch (70%) for quality
  • • Global deployment: 600 GPUs across regions with auto-scaling
  • • Model serving: TensorRT optimization with dynamic batching
  • • Quality assurance: Content moderation, bias detection, accessibility compliance

⚖️ Key Trade-offs & Decisions

  • • Unified vs. specialized models: Single 1.5B model with style conditioning
  • • Latency vs. quality: Multiple model variants (50M mobile to 6B batch)
  • • Accuracy vs. creativity: Task-specific optimization and human preference tuning
  • • Cost vs. scale: $2.3M/month for 100M daily captions with multi-use optimization

🔧 Implementation Challenges

  • • Multi-domain generalization: Medical, artistic, technical content with domain adaptation
  • • Cultural sensitivity: Appropriate descriptions across 25+ languages and cultures
  • • Accessibility compliance: WCAG standards and factual accuracy for screen readers
  • • Real-time constraints: Sub-500ms latency with high-quality outputs

🚀 Alternative Approaches

  • • Template-based generation: Fast but limited creativity and generalization
  • • Retrieval-based captions: High quality but poor novel scene handling
  • • Object detection + NLG pipeline: Modular but misses holistic understanding
  • • Diffusion-based generation: Creative but computationally expensive
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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of image captioning systems in interviews.

1. Multi-Use Case Architecture

"Design an image captioning system serving accessibility (95%+ accuracy), social media (engaging captions), and content management (structured metadata). Address different quality requirements, model architectures, and evaluation frameworks for each use case."

2. Cultural Sensitivity & Bias Mitigation

"Build an image captioning system that works across 25+ languages and cultures while avoiding bias against protected groups. Include training data curation, bias detection, cultural context understanding, and ongoing monitoring systems."

3. Real-time vs Batch Processing

"Design a hybrid system handling both real-time captioning (<500ms) for live applications and high-quality batch processing for content libraries. Address model serving, caching strategies, and quality-latency trade-offs."

4. Multi-Domain Specialization

"Handle diverse image types: photographs, charts/graphs, medical images, artwork, memes, and screenshots. Design domain detection, specialized model routing, and unified quality assessment across different visual domains."

5. Accessibility Compliance

"Build image captioning that meets WCAG accessibility standards for screen reader users. Include factual accuracy requirements, spatial relationship descriptions, and integration with assistive technologies while maintaining processing efficiency."

6. Privacy-Preserving Processing

"Design image captioning for privacy-sensitive scenarios (medical images, personal photos). Address on-device processing, federated learning, differential privacy, and secure cloud processing while maintaining caption quality and system scalability."