Multi-Armed Bandit Testing: How It Works and When to Use

Multi-armed bandit algorithms optimize traffic distribution by shifting visitors toward high-performing variations during tests, utilizing epsilon-greedy, Thompson sampling, and Upper Confidence Bound to manage the explore-exploit tradeoff. Simultaneously, AI observability differs from traditional monitoring by recording prompts, model versions, and latency to explain specific AI outputs. For AI infrastructure, observability extends beyond CPU and memory to monitor GPU efficiency, inference speed, and Kubernetes scheduling to ensure platform health and efficient model serving in production.