MLOps
MLOps is the operational discipline for building, deploying, monitoring, and improving machine learning systems over time.
What it is
MLOps brings software engineering and platform practices into ML lifecycle management: versioning, testing, deployment, monitoring, and controlled updates.
Why it matters for delivery teams
Without MLOps, models often fail after pilot phase because ownership, reproducibility, and monitoring are unclear.
Common mistake
Treating model delivery as a one-time project. Real value depends on ongoing operation, drift detection, and retraining decisions.
Practical next step
Define one lightweight lifecycle: data source ownership, model versioning rules, deployment path, and runtime monitoring.