# Industrial MLOps MLOps for industrial systems is standard MLOps plus one constraint that changes everything: safety. The basic loop is the same as any ML system: monitor for drift, detect degradation, retrain, validate, deploy. Standard tools exist for all of this (CUSUM, KL divergence for drift detection; batch retraining pipelines; model versioning). What makes industrial MLOps hard: - **Knowing when to retrain.** Process conditions drift slowly. A model can degrade for weeks before anyone notices. The detection has to be automated and sensitive enough to catch gradual shifts. - **Knowing what changed.** A sensor drifted? A new raw material batch? A seasonal shift? The diagnosis determines whether you re-estimate parameters, update structure, or fully retrain. - **Validating the retrained model is safe.** In process manufacturing, a bad recommendation can damage equipment or create safety incidents. You can't just A/B test on a live reactor. - **Rollback under constraints.** If the new model behaves unexpectedly, you need instant rollback with safety guarantees during the transition. Each step individually is commodity. The orchestration of all four steps in an automated loop for safety-critical processes is potentially differentiated. When evaluating companies, ask: can you walk me through the last time your system detected drift and triggered recalibration in production without human intervention? Related: [[Model Maintenance at Scale]], [[Digital Twins]], [[Industrial AI MOC]] --- Tags: #deeptech #systems