Series9 parts164 min total
The Multi-Task Fusion Problem: Why Finding the Right Weights is Hard
Part 1 of 9: Understanding how recommendation systems combine multiple prediction scores and why optimizing the fusion weights is challenging.
Writing on ML, Recommendations, and AI Systems
Technical deep-dives on machine learning, recommendation systems, and AI infrastructure.
Foundations
Series5 parts
Multi-Objective Ranking
The first article in a practitioner series on combining prediction scores into a single ranking. Covers the universal pattern, taxonomy of approaches from static weights to RL policies, and when to use each.
Series4 parts
RL for Recommender Systems
Why reinforcement learning matters for recommendations, a refresher on MDP fundamentals, and understanding the spectrum from bandits to full RL.
AUROC and AUPRC: What They Actually Tell You About Your Ranking Model
Five ways to understand AUROC, why AUPRC matters more for imbalanced recsys tasks, and practical guidance for interpreting these metrics in production.
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25 min read
Biases in Large-Scale Recommender Systems
A comprehensive guide to understanding and mitigating the six major biases that affect recommendation systems: selection, position, exposure, popularity, conformity, and feedback loops.
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Optimization
50 min read
LLM Serving from Scratch: The Systems Behind Fast Inference
How LLMs are efficiently served in production — from KV cache management and PagedAttention to speculative decoding and prefill-decode disaggregation.
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Attention Optimization: From Memory Walls to Flash Attention
Why transformer inference is memory-bound, how KV caching eliminates redundant computation, and how FlashAttention achieves 75% GPU utilization through IO-aware tiling.
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