Generating a working (value-generating) machine learning model is not an easy task. It usually involves advanced modelling techniques and teams with scarce skills. However, this is only the first step on an even more complex task: deploying the model into production and preventing its degradation. Even being alleviated by the cloud shift, at least two-thirds of IT spent is still concentrated on maintenance-mode tasks. There is still little research about where this split holds for ML related projects or not, but my take is that this percentage will even increase significantly due to the fact that an ML workload has more "liquid" inputs and fewer control levers as shown below: In essence, maintenance is mainly driven by the level of variability and control we may have over the different components on the system.