MLOps at Enterprise Scale: Lessons from 100+ Deployments

Most ML projects don't fail in the modelling stage — they fail in the operational stage. Feature drift, silent failures, and unmonitored retraining pipelines quietly erode value long after launch day.

Across our engagements, the deployments that hold up over time share three traits: automated monitoring from day one, clear ownership of model lifecycle, and rollback paths that don't require an incident to discover.