Design a production MLOps architecture covering serving, CI/CD, monitoring, and rollback for your ML model.
## CONTEXT A trained model in a notebook delivers no value until it serves predictions reliably. In 2026, MLOps maturity is the difference between teams that ship one model and teams that operate dozens. A sound architecture spans model packaging and registry, a serving pattern matched to latency needs (batch,…
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