Project Case Study

ML Experiment Tracker

Updated: May 2026

Built a CLI-based experiment tracking system to support reproducible ML workflows, enabling structured run logging, metric comparison, and evaluation across experiments.

• Reproducible experiment runs (timestamped)
• Structured metric logging & comparison
• CLI-driven workflow for ML experimentation

Problem

Managing machine learning experiments becomes difficult as runs increase. Without proper tracking, it is hard to reproduce results, compare configurations, and identify the best-performing models.

System Design

• CLI interface for experiment management
• Local JSON-based storage for runs and metadata
• Timestamped run creation for reproducibility
• Metric logging and structured comparison

Workflow

Issue → Branch → Code → Test → PR → CI → Merge → Release

Results & Insights

• Enabled reproducible experiment tracking using structured JSON storage
• Simplified comparison of model performance across runs
• Identified differences in accuracy and loss between baseline and tuned runs
• Improved workflow clarity through CLI-based interaction

Example Output

- baseline | accuracy=0.95, loss=0.42
- tuned    | accuracy=0.97, loss=0.36

Takeaway: Effective ML experimentation requires structured tracking, reproducible runs, and reliable metric comparison across configurations.

Technical Stack

Python · CLI · JSON Storage · PyTest · CI/CD