Machine learning in production is not just about designing and training models. It is a fractured space consisting of a wide variety of tasks ranging from experiment tracking to orchestration, from model deployment to monitoring, from drift detection to feature stores and much, much more than that. Even though there are already some seemingly well-established solutions for these tasks, it can become increasingly difficult to establish a running production system in a reliable and modular manner once all these solutions are brought together.