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medium★ EssentialCapital OneAmazonNetflix

Would you choose blue-green or canary deployment for a new model version, and why?

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.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

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.

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Next in this trackHow do you roll back a bad model in production?Next in this trackDo you have experience deploying ML models on Kubernetes for inference? Walk me through the process and your role.Next in this trackWhat metrics do you autoscale inference pods on, and how do you handle cold starts?
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