# 🔄 ML Lifecycle Management System (MLflow + DagsHub + BentoML)

## 📌 Overview
This project demonstrates a complete machine learning lifecycle pipeline, covering experiment tracking, model versioning, and deployment.

It integrates MLflow for experiment tracking, DagsHub for collaboration and reproducibility, and BentoML for model serving.

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## 🎯 Problem Statement
Managing ML experiments, tracking results, and deploying models efficiently is challenging without proper tooling.

This project solves that by:
- Tracking experiments and metrics systematically  
- Versioning models and datasets  
- Enabling scalable model deployment  

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## 🚀 Features
- Experiment tracking using MLflow  
- Model versioning and reproducibility  
- Integration with DagsHub for collaboration  
- Model deployment using BentoML  
- End-to-end ML lifecycle pipeline  

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## 🛠️ Tech Stack
- Python  
- MLflow  
- DagsHub  
- BentoML  
- Docker  

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## ⚙️ System Architecture
1. Data Ingestion  
2. Model Training  
3. Experiment Tracking (MLflow)  
4. Version Control (DagsHub)  
5. Model Packaging (BentoML)  
6. Deployment and Serving  

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## 🔄 Workflow
1. Train model with different parameters  
2. Log metrics and artifacts using MLflow  
3. Track experiments and compare results  
4. Push code and data to DagsHub  
5. Package model using BentoML  
6. Deploy model as a service  

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## 📊 Results
- Experiments Tracked: XX  
- Best Model Accuracy: XX%  
- Deployment Time: XX minutes  

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## 📂 Project Structure

├── data/ ├── experiments/ ├── models/ ├── deployment/ ├── requirements.txt └── README.md

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## 🔧 Installation
```bash
pip install -r requirements.txt

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▶️ Usage

mlflow ui
bentoml serve

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🧪 Example Output

• Experiment logs with metrics
• Model comparison dashboard
• Deployed model accessible via API

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🔥 Key Highlights

• Complete ML lifecycle management
• Improves reproducibility and collaboration
• Enables production-ready model deployment
• Integrates multiple MLOps tools

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🔮 Future Improvements

• Add CI/CD pipeline integration
• Automate retraining workflows
• Add monitoring and alerting
• Scale deployment using Kubernetes

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🤝 Contributing

Contributions are welcome. Please fork the repository and submit a pull request.

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📜 License

This project is licensed under the MIT License.

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👤 Author

Abhishek Sharma GitHub: https://github.com/brogrammercodes LinkedIn: https://www.linkedin.com/in/abhishek-sharma27012003/