The article discusses the importance of monitoring machine learning models to detect data drift and maintain efficiency. It presents a framework for detecting data drift that involves four stages:
- data retrieval,
- data modeling,
- test statistics calculation,
- hypothesis testing.
The Kubeflow Data Profiler component, a Python library that automates data analysis, monitoring, and sensitive data detection, is used to detect feature drift.
- A pipeline is created using this component to retrieve batches of training and test samples, profile the data, merge the profile model objects, and compute dissimilarity metrics.
- The pipeline returns a difference report containing key-value pairs for several data drift measures.
- The article emphasizes the importance of evaluating data drift and provides actionable steps for detecting and monitoring it using the Kubeflow Data Profiler component.
