Automated MLOps Pipeline for Cost-Effective Classifier Retraining in Response to Data Shifts

This ArXiv article likely presents a novel MLOps pipeline designed to optimize classifier retraining within a cloud environment, focusing on cost efficiency in the face of data drift. The research is likely aimed at practical applications and contributes to the growing field of automated machine learning.

• •Addresses the challenge of retraining machine learning models in response to changing data distributions.
• •Focuses on optimizing cost-effectiveness within a cloud-based MLOps pipeline.
• •Likely offers an automated approach to the model retraining process.