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.

• ✓
  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.