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Core
Contrastive and Metric Learning
Contrastive learning trains embeddings through comparison: pull similar (positive) pairs together and push dissimilar (negative) pairs apart, so distance encodes similarity. It drives retrieval embeddings, CLIP's shared text-image space, face recognition, and self-supervised pretraining. Quality rides on the number and difficulty of negatives. AI, ML, and GenAI engineer interviews probe it because it is how the embeddings under search, RAG, and recommendation are actually trained, and because 'where do good embeddings come from?' has a concrete answer.
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Machine Learning & Data ScienceWhat is contrastive / metric learning, and how does it learn good embeddings?→Machine Learning & Data ScienceWhat is self-supervised learning, and how do contrastive methods and masked prediction work?→System Design for AI in ProductionDesign a system to retrieve similar scenes from a large video corpus given a query clip.→Machine Learning & Data ScienceYou're training embeddings with contrastive/triplet loss. How do you choose pairs, the margin, and negatives?→Machine Learning & Data ScienceYour churn model's AUC jumps from 0.71 to 0.93 after adding a 7-day rolling feature. What now?→ML System Design (Product)Design a music recommendation system (Spotify-style).→
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