I came in ready to talk about video ingestion, CDN, adaptive bitrate streaming, all that stuff.
Start by clarifying requirements and scale, then design a high-level architecture covering video ingestion, storage, search, and recommendation. Focus on how AI components (search ranking, recommendation models) integrate with the system, and discuss trade-offs between relevance, latency, and cost.
Pro tip: Emphasize how you would measure and iterate on the AI models using online metrics (CTR, watch time) and offline evaluation, and how you'd handle cold-start and feedback loops to avoid bias.
Ask questions to understand scale (users, videos, QPS), key features (search, recommendations), and constraints (latency, budget). Define success metrics like user engagement and satisfaction.
Outline core components: video upload/transcoding, storage (object store + CDN), metadata DB, search index, recommendation service, and user profile store. Sketch data flow from ingestion to serving.
Design search using inverted index (e.g., Elasticsearch) with text and metadata. Incorporate AI for query understanding (embeddings, semantic search) and learning-to-rank for relevance.
Design a two-stage recommender: candidate generation (collaborative filtering, content-based) and ranking (deep learning model). Include real-time personalization using user embeddings and session data.
Discuss trade-offs: batch vs. real-time inference, model complexity vs. latency, cost of GPU vs. CPU. Address scaling via sharding, caching, and asynchronous processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.