MLOps
MLOps is the practice of reliably deploying, monitoring and maintaining machine-learning models in production — the discipline that keeps industrial AI working after the pilot. It covers versioning, retraining, monitoring for drift and governance, so models stay accurate as conditions change.
Many industrial AI projects succeed as a pilot then quietly fail because the model degrades as equipment, processes or data change. MLOps applies software-engineering rigour to models: tracking versions and data, automating retraining, monitoring prediction quality and drift, and governing changes. It is what turns a one-off model into a dependable production system — increasingly important as plants deploy more AI.
Related terms
Machine Learning (Industrial) · Anomaly Detection · Predictive Maintenance (PdM)