Welcome to a hands-on demo project that showcases how to build a production-grade Machine Learning pipeline from scratch. This project is designed to help you understand and explore:
- 📦 DVC for data & model versioning
- ⚙️ MLflow for experiment tracking
- ☁️ DagsHub for seamless collaboration and remote tracking
🎯 Objective: Train a robust Random Forest Classifier 🌲 on the Pima Indians Diabetes Dataset 🧬, with a modular and reproducible ML pipeline including:
- 🔍 Data Preprocessing
- 🧠 Model Training
- 📈 Model Evaluation
🌐 🔗 View Live Project on DagsHub – Explore how DVC, MLflow, and remote pipelines work together in a real-world ML pipeline demo!
With DVC, you can:
- 🧬 Track datasets, models, and code changes
- ⚙️ Structure workflows into stages (preprocess ➡️ train ➡️ evaluate)
- 🔁 Automatically re-run affected stages when changes occur
- ☁️ Connect to remote data storage (DagsHub/S3) for collaboration
🛠️ Your pipeline becomes:
- ✅ Modular
- ✅ Reproducible
- ✅ Scalable
MLflow allows:
- 🧪 Tracking experiments: log parameters, metrics, models
- 🧮 Comparing runs visually
- 📏 Optimizing hyperparameters (
n_estimators,max_depth, etc.) - 📦 Storing & reusing model artifacts
🔍 “What gets measured gets improved.” — With MLflow, you measure everything.
- Pima Indians Diabetes Dataset
📊 Medical data for binary classification
✅ Balanced features
✅ Real-world healthcare relevance
- 🌲 Random Forest Classifier
✅ Robust
✅ Handles missing values well
✅ Performs well on tabular data
At the end of this project, you will have:
- 🎯 A complete ML pipeline versioned with DVC
- ⚙️ Multiple model experiments tracked via MLflow
- ☁️ Integrated remote storage (DagsHub)
- 🔁 Reproducible and scalable pipeline stages
- 🐍 Python
- 📦 DVC
- ⚙️ MLflow
- ☁️ DagsHub
- 📊 Scikit-learn
- 🧪 Pandas, NumPy, Matplotlib
Feel free to fork, ⭐ star, or raise issues!
Together, let’s build smarter pipelines 🔁💡
✅ Built with ❤️ by Anand – Follow for more end-to-end ML & MLOps content!