rollout

They answer different questions, and mixing them up costs you the round: one tests your engineering, the other tests your model's business impact. What each can and cannot catch, and the order a strong candidate always runs them in.

Reported from Capital One ML loops, and the symmetric 'both have pros and cons' answer fails it. Why model failures being statistical makes canary the default, the two cases where blue-green wins, and the entity-randomization detail that survives follow-ups.