This project aims to deploy a machine learning classification model to predict customer license status using Azure DevOps. It demonstrates how to implement Continuous Integration (CI) and Continuous Delivery (CD) pipelines for machine learning projects.
For a comprehensive guide on how to deploy and use this project, please visit our Project Wiki.
The project focuses on deciding whether a customer's license should be issued, renewed, or cancelled based on various parameters. It utilizes past data to train the model to predict future outcomes accurately.
The challenge is to develop a machine learning model that can learn from training data and accurately predict license statuses ('Issued', 'Renewed', 'Cancelled') based on the features provided.
- Python: For data processing and modeling.
- h2O, Scikit-learn, TensorFlow, Keras: For building and training the machine learning models.
- Azure DevOps: For implementing CI/CD pipelines, managing the workflow, and deploying the model.
The model is deployed using Docker containers, orchestrated through Azure Pipelines to enable scalable and efficient model serving.
Deep-Neural-Network.py: Main Python script for model training and evaluation.Dockerfile: Docker configuration for creating the environment to run the model.Model_Api.py: Flask API setup for model deployment.requirements.txt: Lists dependencies to be installed.
To run this project, you'll need to set up Azure DevOps and configure your pipeline according to the instructions in the Model_Api.py and Dockerfile.
Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
This project is licensed under t