Greens.ai

This project is a web application for users to classify vegetables using machine learning models. The main focus is on implementing MLOps practices, including the integration of a Continuous Integration and Continuous Deployment (CI/CD) pipeline.

The web application allows users to upload images of vegetables and receive predictions on their types or classes. It leverages machine learning models trained on vegetable datasets to perform classification tasks. The project emphasizes MLOps practices to ensure efficient model development, deployment, and maintenance.

• Developing and deploying models for classifying vegetables based on images
• Incorporating practices into the project for efficient model development, deployment, and maintenance
• Continuous Integration and Continuous Deployment (CI/CD): Setting up and managing a CI/CD pipeline using tools like GitLab to automate testing and deployment
• Containerizing applications with Docker to ensure consistency and portability across different environments
• FDeveloping the frontend of the web application using React for building user interfaces
• Training models using TensorFlow, a widely-used open-source machine learning framework
• Developing the backend of the web application using Flask to handle image uploads and predictions
• Implementing testing using Pytest to ensure reliability and accuracy of the models and application functionality.