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.
In context and practice
In practice, mlops spans both strategy and software. It is central to guides like How to start using AI in your industrial business, and essential to how Cognite Data Fusion, Seeq and similar platforms operate. Plants use mlops to bridge operations and technology decisions.
Closely related terms include Machine Learning (Industrial), Anomaly Detection, Predictive Maintenance (PdM). These concepts often work together in industrial practice — mastering one usually means understanding all of them.
In your plant: When planning maintenance, reliability or efficiency projects, clarify your approach to mlops. Ask vendors or consultants how they implement it. The specifics matter — two plants with the same definition of mlops may execute it very differently based on their equipment, age, and operational culture. The gap between definition and execution is where real value (or waste) lives.
Measuring success: Mlops programs succeed when you can measure their impact. Set a baseline, implement the practice, and track the outcome — downtime reduction, energy savings, cost avoidance, or compliance improvement. Most plants find that a 3–6 month pilot clarifies the true value and ROI of mlops. Don't guess; measure.
Why it matters: mlops is not an end in itself, but a lever in your plant's overall efficiency and reliability strategy. It works best when part of a system: clear ownership, investment in tools or training, executive sponsorship, and regular review. Isolated initiatives often fizzle. Embedded mlops programs compound, delivering value year after year as the practice matures and spreads.
Related terms
Machine Learning (Industrial) · Anomaly Detection · Predictive Maintenance (PdM)