MLOps with enhanced performance control and observability
This work addresses reliability issues in MLOps for practitioners, but it appears incremental as it focuses on integrating existing tools into a pipeline.
The paper tackles the problem of MLOps system failures due to increasing data complexity by introducing tools for observability, such as data drift detection and model version control, to build a more robust system.
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.