What shipped

• ✓Implemented automated logging of hyperparameters and metrics for comparative analysis.
• ✓Used MLflow Model Registry to version and stage ML models through development → staging → production lifecycles.
• ✓Integrated MLflow tracking server with S3-backed artifact storage for team-wide experiment sharing.

Overview

Explored MLflow for lifecycle management of machine learning models, focusing on experiment tracking, parameter logging, and artifact storage to ensure reproducibility in model training pipelines.