MLWhiz | AI Unwrapped

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3 Modern Approaches to Solving Cold Start in RecSys
Contextual bandits, meta-learning, and LLMs — how Spotify, TikTok, and YouTube handle new users and items. The practitioner's guide to cold start.
 • Rahul Agarwal
MLWhiz Weekly AI/ML Newsletter # 2
Here is what happened this week.
 • Rahul Agarwal
From Candidates to Clicks: The Engineering Anatomy of Ranking
How modern recommendation systems go from 1,000 candidates to the one item you actually tap
 • Rahul Agarwal
MLWhiz Weekly AI/ML Newsletter # 1
Here is what happened this week.
 • Rahul Agarwal
Vector Search at Scale: The Production Engineer's Guide
IVF partitions space. PQ compresses memory. Together they make 100M vector search actually possible
 • Rahul Agarwal
How YouTube Finds Your Next Video in Milliseconds
A deep dive into two-tower retrieval, in-batch negatives, and the tricks that make it work
 • Rahul Agarwal
The 3-Stage Funnel Behind Every Modern Recommender System
Two-Tower models, vector databases, cross-encoders—and how they work together at scale
 • Rahul Agarwal
How Recommendation Systems Learned to Think
Recsys Series Part 2: From collaborative filtering breakthroughs to generative AI agents that can chat about your preferences and explain their…
 • Rahul Agarwal
RecSys Fundamentals: The Art and Science of Digital Matchmaking
Recsys Series Part 1: Master the three core approaches that power every modern recommendation engine
 • Rahul Agarwal
MLWhiz | AI Unwrapped
MLWhiz | AI Unwrapped
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