Designing a Visual Search System at Scale | ML SystemDesign
🚀 In this video, we break down the end-to-end system design of a Visual Search Engine, similar to platforms like Google Lens or Pinterest.
If you're preparing for Machine Learning System Design interviews or building real-world AI systems, this video covers everything you need—from feature extraction to scalable retrieval systems.
📌 What you'll learn:
What is Visual Search and how it works
Image embeddings using deep learning models
Feature extraction with CNNs / Vision Transformers
Similarity search using vector databases (FAISS, Annoy, etc.)
Indexing and retrieval pipelines
Real-time vs batch processing trade-offs
Scaling the system for millions of images
Latency, accuracy, and cost optimizations
Production considerations and architecture
🧠 Concepts covered:
Embeddings & vector similarity
Approximate Nearest Neighbor (ANN) search
Metadata filtering
Ranking systems
Caching strategies
Distributed systems for ML
💡 Use cases:
E-commerce product search
Reverse image search
Fashion & recommendation systems
Visual discovery platforms
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