mlflow-monitor

What problem does it solve?

This Skill provides a live, interactive dashboard for monitoring machine learning experiments, allowing users to track metrics, compare runs, and gain insights into model performance during training.

Core Features & Use Cases

• Launch MLflow UI: Starts a local MLflow server for real-time experiment visualization.
• Live Monitoring: Enables users to observe training progress and key metrics as they are logged.
• Run Comparison: Facilitates side-by-side comparison of different training runs to identify optimal configurations.
• Use Case: When training multiple deep learning models, use this Skill to launch the MLflow UI and monitor the accuracy, loss, and other metrics of each model in real-time, helping you decide which model to proceed with.

Quick Start

Launch the MLflow UI to monitor your current training experiments.