Design an online experimentation (A/B testing) platform for ML models at scale.
A dependable experiment platform goes well beyond splitting traffic in half. What matters is stable assignment, exposure logging, statistical discipline, and guardrails that hold up against peeking and sample-ratio mismatch. Here is the design.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A dependable experiment platform goes well beyond splitting traffic in half. What matters is stable assignment, exposure logging, statistical discipline, and guardrails that hold up against peeking and sample-ratio mismatch. Here is the design.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.