Managing Machine Learning Life cycle with MLflow

The life cycle of a machine learning project is complex. In the paper Hidden Technical Debt in Machine Learning Systems, Google took the reference of the software engineering framework of technical debt and explained that the maintenance of real-world ML systems can incur massive costs. The below image truly depicts the real scenario. The sandwiched tiny black box, surrounded by big boxes is the Magic Machine learning Code:) and to run this magic code in the production, we need to deal with several other processes e.g. Apart from that when the ML system is in the exploration phase, a team of data scientists/ML engineers keep close eyes on the metrics and performance of the different models to get an optimized one.