{"repo":"m-nanda/End-to-End-ML","free":true,"listed":false,"github":"https://github.com/m-nanda/End-to-End-ML","clone":"git clone https://github.com/m-nanda/End-to-End-ML.git","description":"An \"End-to-End Machine Learning\" project focuses on building a machine learning pipeline that prevents data leakage and deploys the model with microservice-architecture for real-world use.","language":"Python","stars":16,"topics":["api","iris-classification","iris-data","machine-learning","microservices-architecture","data-visualization","flask","python","sklearn","streamlit"],"license":"MIT","category":"machine-learning","readme_excerpt":"End-to-End Machine Learning Project Description Building a machine learning pipeline that is effective and reliable can be a challenging task. One of the critical challenges is data leakage, where information from the future or target variable is inadvertently leaked into the training data, leading to over-optimistic model performance metrics. Another challenge is deploying the final model for real-world use, which requires integrating the model into an existing system and ensuring consistent performance over time. This project provides an end-to-end process to address these challenges, from building to deploying machine learning models. The pipeline is designed to prevent data leakage and generate an automatic report for each run. Then, the model deploys as an API that can be used in a web app. This approach is to make model deployment more efficient and more manageable. It can be applied in a microservice architecture. In short, here are some interesting features that can be found in this project: A machine learning pipeline model that prevents data leakage and generates an automatic report (development stage). Microservices architecture approach consists of Authentication, Machine Learning, and Web App service. Consider a security with authentication and hiding credentials. Objective The objective of this project is: - To build a machine learning pipeline that addresses data leakage issues. - To deploy the model in the production stage for real-world use. - To document the","default_branch":null,"files":null,"tree":[],"storefront":"/r/m-nanda","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/m-nanda/End-to-End-ML/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}