RT if this helps your interview prep! 🔄
ROC-AUC, F1-Score, Precision/Recall, Log-Loss.
Case Study: Designing a Video Recommendation System (YouTube/TikTok Style) RT if this helps your interview prep
Alex Xu’s new blueprint for ML Engineers
User interactions, database snapshots, or third-party APIs. and user-item interaction.
Spend significant time discussing data preprocessing and feature engineering.
With the industry shifting from "model-first" to "production-first" thinking, interviewers aren't just asking about architecture anymore. They are asking about: ⟶ Feature Stores & Data Pipelines ⟶ Model Training Infrastructure ⟶ Online vs. Offline Evaluation ⟶ Scaling Inference & Monitoring RT if this helps your interview prep
Focuses on candidate generation vs. ranking, handling sparsity, and user-item interaction.