A coursework project demonstrating manual machine learning experiment tracking and Git-based version control practices using Decision Tree experiments on the Palmer Penguins dataset.
This project was completed as part of DSC524 – Designing MLOps for Enterprises in the M.Tech Artificial Intelligence and Data Science program at IIIT Kottayam.
The objective of this project was to understand foundational MLOps practices by tracking changes across multiple machine learning experiments.
The project focuses on:
- Git-based version control
- Experiment tracking
- Hyperparameter experimentation
- Preprocessing variations
- Feature-selection variations
- Model evaluation
- Comparing experiment results
The experiments use the Palmer Penguins dataset, containing observations of three penguin species:
- Adelie
- Chinstrap
- Gentoo
The dataset includes physical and categorical characteristics such as bill measurements, flipper length, body mass, island, and sex.
Detailed dataset information is available in dataset_info.md.
Four Decision Tree experiments were manually tracked with variations in:
- Hyperparameters
- Preprocessing
- Feature selection
- Precision
- ROC-AUC
The best recorded experiment achieved:
- Precision: 0.99
- ROC-AUC: 0.99
The complete experiment table is available in:
| Experiment | Configuration | Precision | ROC-AUC |
|---|---|---|---|
| EXP-01 | Baseline Decision Tree | 0.97 | 0.97 |
| EXP-02 | Max Depth = 5 + preprocessing | 0.98 | 0.98 |
| EXP-03 | Max Depth = 7 + preprocessing + imputation | 0.99 | 0.99 |
| EXP-04 | Pruned Decision Tree | 0.98 | 0.99 |
Baseline Experiment
↓
Record Configuration & Metrics
↓
Modify Hyperparameters / Preprocessing
↓
Run New Experiment
↓
Compare Results
↓
Track Changes with Git
↓
Document Findings
mlops-git-experiment-tracking/
├── README.md
├── analysis.md
├── dataset_info.md
├── experiment_tracking.md
└── .gitignore
README.md— Project overview and experiment summaryanalysis.md— Findings from the recorded experimentsdataset_info.md— Palmer Penguins dataset informationexperiment_tracking.md— Manual experiment tracking table
This project demonstrates foundational MLOps concepts including:
- Tracking ML experiments systematically
- Comparing model configurations
- Recording evaluation metrics
- Managing experiment changes using Git
- Documenting model-development decisions
This repository focuses on Git-based workflow and manual experiment tracking.
The model-training implementation is not included in this repository. More advanced experiment tracking using tools such as MLflow is covered in later MLOps projects.
Anupam K Ajith AI/ML & GenAI | M.Tech AI & Data Science @ IIIT Kottayam | Python | RAG | MLOps