MLOps
The practices for getting machine learning models into production and keeping them working.
MLOps applies DevOps thinking to ML, with extra problems: data versioning, training reproducibility, drift monitoring, and retraining pipelines. The insight it encodes is that a model in a notebook is roughly 10% of the work.
In practice: Automatic retraining triggered when monitored accuracy drops below a threshold.