Overview

The project presents an intriguing opportunity for those interested in machine learning and web development. It showcases the deployment of an Iris classification model using the Flask web framework and is hosted on Azure App Service. This deployment not only highlights the technical aspects but also serves as a template for individuals looking to contribute to similar machine learning projects within the Daytona framework.

As an open-source contribution initiative, it invites developers to engage with the project, potentially enhancing it further while gaining practical experience. The clean and minimal user interface makes the model's functionality accessible, allowing users to explore its features seamlessly.

Features

Flask Web Framework: Utilizes Flask for a lightweight and user-friendly interface that simplifies interactions with the classification model.
Azure App Service Deployment: Fully deploys the application on Azure, providing reliable cloud hosting and scalability options.
Comprehensive Documentation: Offers a README file that serves as a template for creating and contributing sample repositories, facilitating easier onboarding for new developers.
Preprocessing Pipeline: Integrates data preparation steps into the web project for streamlined functionality, essential for machine learning applications.
Visual Representation of Output: Features a visually appealing dashboard that offers insight into model predictions and classifications.
Open for Contributions: Encourages collaboration by allowing others to contribute to the project and improve its capabilities over time.
Dynamic and CI/CD Deployment Options: Plans to introduce methods for continuous integration and delivery, optimizing the deployment process.

Kittupriyatham

Machine Learning Model Deployment