LEGACY PROJECT · EXPERIMENT TRACKING
ML Experiment Tracker
Updated: May 2026
Built a CLI-based experiment tracking system for reproducible ML workflows, structured run logging, metric comparison, and evaluation across experiments.
Problem
As experiment counts grow, unstructured records make it difficult to reproduce results, compare configurations, and select the strongest run.
Technical Highlights
Python · CLI · JSON Storage · PyTest · CI/CD
- Timestamped, reproducible experiment runs
- Structured metric logging and comparison
- CLI-driven experimentation workflow
System Design
- CLI interface for experiment management
- Local JSON storage for runs and metadata
- Timestamped run creation for reproducibility
- Structured metric logging and comparison
Workflow
Create Run → Record Configuration → Log Metrics → Persist Results → Compare Runs → Select Best Result
Results and Insights
- Enabled reproducible tracking through structured JSON storage.
- Simplified model-performance comparison across runs.
- Exposed accuracy and loss differences between baseline and tuned runs.
- Improved workflow clarity through CLI 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.