8 Hazards Menacing Machine Learning Systems in Production

It is not easy to develop and deploy machine learning models, and even less so to integrate them with the surrounding data pipelines to build large-scale ML systems. The hardest part, however, comes later, when the entire system has been tested, deployed, and is up and running. For deployment is by no means the end of the journey. Much to the contrary, this is when a new challenge starts: maintenance. Maintenance costs of machine learning systems, by which I mean the time engineers use to keep the systems alive and unflawed, may become exorbitant in some cases.