Scoring millions of candidate ads in 40ms is impossible. Break the system into two stages:
Traditional system design focuses on scalability, databases, sharding, and microservices. ML system design includes these elements but adds complex algorithmic dependencies.
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Traditional system design focuses on scalability, availability, data consistency, and latency. You deal with load balancers, databases, caching layers, and microservices.
Set up automated pipelines to collect new labels from user interactions. Scoring millions of candidate ads in 40ms is impossible
[ User Interaction ] │ ▼ ┌───────────────┐ │ 1. Retrieval │ ──► Filters millions of videos down to ~100 candidates └───────────────┘ (Using simple embeddings, Two-Tower models) │ ▼ ┌───────────────┐ │ 2. Ranking │ ──► Scores and ranks the 100 candidates └───────────────┘ (Using deep neural networks, heavy features) │ ▼ ┌───────────────┐ │ 3. Re-ranking │ ──► Applies business logic, filters duplicates, └───────────────┘ ensures diversity, removes explicit content │ ▼ [ Final Feed ] Scale Constraints 1 billion active users. 100 million videos available.
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Architectural Deep Dive: Ad Click-Through Rate (CTR) Prediction
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It helps you move away from treating every problem as a new, unique scenario and instead use proven architectural patterns. Key Components of a Successful ML System Design