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.

1. Clone the repository:

   git clone https://github.com/karimosman89/ML-Pipeline-AWS.git
   cd ML-Pipeline-AWS
   

2. Install the required Python packages:

   pip install -r requirements.txt
   

3. Build the Docker image:

   docker build -t ml-pipeline-aws.
   

1. Preprocess the data:

    python src/preprocess.py
   

2. Train the model:

   python src/train_model.py
   

3. 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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