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+-------------------+ | Inference Engine | +---------+---------+ | v +-------------------+ +-------------------+ +-------------------+ | Feature Store | ---> | Model Registry | <--- | Training Pipeline | +-------------------+ +-------------------+ +-------------------+ ^ | +--------+----------+ | Data Warehouse / | | Data Lake | +-------------------+ The Feature Store
3. Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, & Michael Munn
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Review foundational industry papers, including Deep Neural Networks for YouTube Recommendations (Covington et al.) and Ad Click Prediction: a View from the Trenches (McMahan et al.).
Reduce the pool from millions of videos to hundreds using a Two-Tower Neural Network architecture. One tower generates user embeddings based on historical behavior, while the parallel tower generates video embeddings. Calculate the dot product between these embeddings and use an Approximate Nearest Neighbor (ANN) search engine to find the top candidates instantly.
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: Mention near-line processing for dynamic streaming updates. 7. Monitoring, Evaluation, and Iteration An ML system is never truly finished after deployment:
Select appropriate loss functions (e.g., Cross-Entropy, Contrastive Loss, or Pairwise Ranking Loss).
: Scaling content delivery to millions of users. Availability and Access Reduce the pool from millions of videos to
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Tie technical success to business value using A/B testing frameworks, monitoring metrics like CTR, conversion rates, and revenue lift. 6. Deployment & Serving Infrastructure Explain how the model will handle production-scale traffic.