Machine Learning System Design Interview — YouTube Video Search

1. 1Design a high-level architecture for video search using machine learning
2. 2Implement a data ingestion pipeline to process video metadata
3. 3Build a model to learn video embeddings for efficient similarity search
4. 4Configure a vector database for storing and querying video embeddings
5. 5Test and evaluate the search system using relevant metrics

Machine learning engineers and software developers designing scalable search systems can benefit from this knowledge to improve video retrieval efficiency

💡 Efficient video search requires a combination of data ingestion, model-based embeddings, and vector database querying

Design a scalable ML system for YouTube video search!