  The explosion of data and its ever increasing complexity in the last few
years, has made MLOps systems more prone to failure, and new tools need to be
embedded in such systems to avoid such failure. In this demo, we will introduce
crucial tools in the observability module of a MLOps system that target
difficult issues like data drfit and model version control for optimum model
selection. We believe integrating these features in our MLOps pipeline would go
a long way in building a robust system immune to early stage ML system
failures.
