MLOps with enhanced performance control and observability

TL;DR

This paper introduces enhanced tools for MLOps systems focusing on observability, data drift detection, and model version control to improve robustness and prevent failures in complex data environments.

Contribution

It presents new observability tools integrated into MLOps pipelines that address data drift and model management challenges, improving system reliability.

Findings

Enhanced observability modules improve failure detection.

Tools effectively monitor data drift and model versions.

Integration leads to more robust MLOps systems.

Abstract

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.