MLOps: Why data and model experiment tracking is important ?

With the rise of interest and the number of machine learning projects (self-driving car, facial recognition, recommendation systems), traditional software development has shifted from hard-coded rules to data-estimated rules a.k.a. A set of new challenges arose for building reliable and stable information systems that rely on imperfect data-driven models, such as model versioning, deployment, monitoring, explainability and reproducibility. There is a whole new set of software engineering best practices that comes with the use of data-driven models in order to tackle those challenges, called MLOps. In order to get a broader view of what MLOps is, I recommend you to take a look at Jamila's article: Why MLOps is so important to understand?. The main purpose of MLOps is to make your entire ML project lifecycle automated and reproducible.