Practice — MLOps Model Lifecycle & Governance (6 questions)

A regulator asks your lending company to explain a specific credit decision made 14 months ago: "User X was denied a loan on 2025-06-20. Explain the decision, including which model made it and why it was trusted to be in production." Your prediction logs retain only 90 days of history, and your model registry only keeps the current production version of each model — older versions are deleted once a newer one is promoted, to save storage.

1. Walk through, step by step, what you would need to reconstruct the full chain from "the decision" back to "why the model was trusted," and identify exactly where your current setup breaks the chain.
2. Propose a retention policy (what to keep, how long) that would have prevented this gap, and justify the durations.
3. Your CTO argues "we can't keep every old model artifact forever, storage isn't free." How do you respond, and is there a legitimate version of the concern worth addressing?