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https://github.com/jayanth119/deliverymlops
First mlops project based on mlops principals
https://github.com/jayanth119/deliverymlops
Last synced: about 2 months ago
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First mlops project based on mlops principals
- Host: GitHub
- URL: https://github.com/jayanth119/deliverymlops
- Owner: jayanth119
- License: mit
- Created: 2024-07-13T17:07:06.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-13T17:23:47.000Z (6 months ago)
- Last Synced: 2024-07-13T18:29:50.321Z (6 months ago)
- Language: Python
- Size: 14.7 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer Price Prediction MLOps Project
This project aims to predict customer prices using a machine learning pipeline orchestrated with ZenML, tracked and logged using MLflow, and deployed with a Streamlit app for interactive user interface.
## Table of Contents
1. [Project Overview](#project-overview)
2. [Technologies Used](#technologies-used)
3. [Project Structure](#project-structure)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Contributing](#contributing)
7. [License](#license)
## Project Overview
Customer Price Prediction is a critical component for businesses to strategize their pricing models. This project automates the machine learning workflow, from data ingestion to model deployment, ensuring reproducibility and scalability.
### High Level Overview
### Training and Deployment Pipeline
## Technologies Used
- **ZenML**: For orchestrating the machine learning pipeline.
- **MLflow**: For tracking experiments, logging models, and managing lifecycle.
- **Streamlit**: For creating an interactive web application to showcase model predictions.
## Project Structure
```
customer-price-prediction/
│
├── data/
│ ├── olist_customers_dataset.csv # Raw data files
│
│
│
├── src/
│ ├── data_ingestion.py # Script for data ingestion
│ ├── preprocessing.py # Script for data preprocessing
│ ├── training.py # Script for model training
│ ├── evaluation.py # Script for model evaluation
│ ├── deployment.py # Script for model deployment
│
├── streamlit_app.py # Streamlit app script
│
├── zenml_pipeline.py # ZenML pipeline definition
│
├── mlflow_experiments/ # MLflow experiment logs
│
├── requirements.txt # Python dependencies
│
├── README.md # Project README file
│
└── .gitignore # Git ignore file
```
## Installation
To get a copy of this project up and running on your local machine, follow these steps:
1. **Clone the repository**
```bash
git clone https://github.com/jayanth119/DeliveryMlops
cd DeliveryMlops
```
2. **Create a virtual environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Install ZenML**
```bash
pip install zenml
zenml init
```
5. **Install MLflow**
```bash
pip install mlflow
```
6. **Install Streamlit**
```bash
pip install streamlit
```
## Usage
1. **Run the ZenML pipeline**
```bash
python zenml_pipeline.py
```
2. **Track experiments with MLflow**
Start the MLflow server:
```bash
mlflow ui
```
Access the MLflow UI at `http://localhost:5000`.
3. **Launch the Streamlit app**
```bash
streamlit run streamlit_app.py
```
Access the Streamlit app at `http://localhost:8501`.
## Contributing
We welcome contributions to enhance the functionalities and features of this project. Please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Open a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
### Feature Importance
---
Feel free to customize this template to fit your project's specific details and requirements.