Article Series

Adaptive Optimization at Scale: Contextual Bandits from Theory to Production

This series covers the full journey of contextual bandits — from knowing when to reach for them over A/B tests or full RL, through the mathematical foundations, algorithm implementations, neural extensions, and finally production deployment with offline evaluation and monitoring.

5 parts · ~113 min total reading time · Last updated Nov 2025

Part 1

When to Use Contextual Bandits: The Decision Framework

When to use bandits vs A/B tests vs full RL. Build the mental model for choosing the right optimization approach.

Part 2

Contextual Bandit Theory: Regret Bounds and Exploration

Regret bounds, reward models, and the exploration-exploitation tradeoff — the theory that makes bandit algorithms work.

Part 3

Implementing Contextual Bandits: Complete Algorithm Guide

Epsilon-greedy, UCB, LinUCB, Thompson Sampling — implementations and tuning for each algorithm.

Part 4

Neural Contextual Bandits for High-Dimensional Data

Bootstrap uncertainty, embeddings, and two-stage selection for scaling bandits with neural networks.

Part 5

Deploying Contextual Bandits: Production Guide and Offline Evaluation

Systems design, offline evaluation, and monitoring — everything needed to run contextual bandits in production.

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