Architect a real-time multi-armed bandit and compare trade-offs to A/B testing
WHAT IT TESTS: Real-time ML serving and statistical trade-offs. ANSWER OUTLINE: Sketch a fast arm router, streaming feedback, and model updates; contrast MAB regret minimization with A/B's unbiased estimates.
WHAT IT TESTS: Whether you can bridge experimentation theory with production distributed systems. ANSWER OUTLINE: First, describe a <100ms assignment service using epsilon-greedy or Thompson Sampling; second, add an event pipeline that ingests rewards and updates arms; third, explain that MAB minimizes regret by shifting traffic to winners dynamically, while A/B tests use a fixed 50/50 split for unbiased estimates at higher opportunity cost.
Read the original → optimizely.com
- #experimentation
- #machine-learning
- #system-design
- #ab-testing
- #real-time
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