Azure ML Pipeline

Description

This project provides a comprehensive setup for training, evaluating, and deploying machine learning models using Azure Machine Learning. It includes scripts for data preprocessing, model training, evaluation, deployment, and monitoring.

Installation

Clone the repository:

git clone https://github.com/danielhamelberg/azure-ml-pipeline.git

Navigate to the project directory:
```
cd azure-ml-pipeline
```
Install the dependencies:
```
pip install -r requirements.txt
```

Usage

Setting Up the Environment

Create an Azure Machine Learning Workspace:

python environment_setup/setup_workspace.py

Data Ingestion

Upload data to Azure Blob Storage and register it as a dataset:
```
python data_ingestion/data_ingestion.py
```

Data Preprocessing

Preprocess the data:

python data_preprocessing/data_preprocessing.py

Model Training

Train the model using Azure AutoML:
```
python model_training/train_model.py
```

Model Evaluation

Evaluate the trained model:

python model_evaluation/evaluate_model.py

Model Deployment

Deploy the model as a web service:
```
python model_deployment/deploy_model.py
```

Monitoring

Set up monitoring for the deployed model:
```
python monitoring/monitoring_setup.py
```

Contributing

Fork the repository.
Create a new branch:
```
git checkout -b feature/your-feature
```
Make your changes and commit them:
```
git commit -m 'Add some feature'
```
Push to the branch:
```
git push origin feature/your-feature
```
Open a pull request.

License

This project is licensed under the GNU General Public License v3.0.

Contact

Created by Daniel Hamelberg - feel free to contact me!