This project implements an end-to-end machine learning pipeline designed to predict customer churn using AWS services. The pipeline includes data ingestion, preprocessing, model training, deployment, and monitoring, providing a robust framework for developing and deploying machine learning models in the cloud.
- Data Ingestion: Load data from various sources (e.g., Amazon S3, CSV files).
- Data Preprocessing: Clean and preprocess the data to prepare it for model training.
- Model Training: Train a machine learning model using AWS SageMaker.
- Model Deployment: Deploy the trained model as a REST API using AWS SageMaker.
- Monitoring: Set up monitoring and logging for performance evaluation and alerts.
ML-Pipeline-AWS/
│
├── data/
│
└── customer_data.csv
│
├── src/
│
├── preprocess.py
│
├── train_model.py
│
└── deploy_model.py
│
├── Dockerfile
├── requirements.txt
└── .github/
└── workflows/
└── aws_pipeline.yml
- AWS Account: Ensure you have an AWS account set up with permissions to use SageMaker.
- Docker: Install Docker to build and run containers.
- Python: Version 3.8 or higher is recommended.
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Clone the repository:
git clone https://github.com/karimosman89/ML-Pipeline-AWS.git cd ML-Pipeline-AWS
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Install the required Python packages:
pip install -r requirements.txt
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Build the Docker image:
docker build -t ml-pipeline-aws.
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Preprocess the data:
python src/preprocess.py
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Train the model:
python src/train_model.py
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Deploy the model:
python src/deploy_model.py
The project includes a GitHub Actions workflow (aws_pipeline.yml) that automates the CI/CD process for the pipeline. It runs on code pushed to the main branch and executes the following steps:
This project is licensed under the MIT License. See the LICENSE file for details.
1- AWS Documentation for resources on AWS services. 2- Scikit-learn for machine learning algorithms.
- Overview: Added an introductory section explaining the project's purpose and functionality.
- Structured Components: Enhanced clarity by specifying what each component entails.
- Project Structure: Improved formatting and descriptions to make it more informative.
- Getting Started Section: Added prerequisites, installation steps, and usage instructions for easy understanding.
- GitHub Actions Section: Clarified how CI/CD is implemented in the project.
- License and Acknowledgments: Sections for licensing and acknowledgment are included, which are good practices for open-source projects.
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