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height=\"100\"\u003e\n\n# mlops\nOpen-source tool for **tracking** \u0026 **monitoring** machine learning models. \n\n![FastAPI](https://img.shields.io/badge/FastAPI-009688.svg?style=for-the-badge\u0026logo=FastAPI\u0026logoColor=white)\n![React](https://img.shields.io/badge/react-%2320232a.svg?style=for-the-badge\u0026logo=react\u0026logoColor=%2361DAFB)\n![MongoDB](https://img.shields.io/badge/MongoDB-%234ea94b.svg?style=for-the-badge\u0026logo=mongodb\u0026logoColor=white)\n![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge\u0026logo=docker\u0026logoColor=white)\n![PyPI](https://img.shields.io/badge/PyPI-3775A9.svg?style=for-the-badge\u0026logo=PyPI\u0026logoColor=white)\n\n[![PyPI version](https://badge.fury.io/py/mlops-ai.svg)](https://badge.fury.io/py/mlops-ai)\n[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\n## Table of Contents\n- [Introduction](#introduction)\n- [Explanatory video](#explanatory-video)\n- [Installation \u0026 usage](#installation--usage)\n- [Technologies](#technologies)\n- [Documentation](#documentation)\n- [Examples](#examples)\n- [License](#license)\n- [Contact](#contact)\n- [References](#references)\n- [To-Do](#to-do)\n\n## Introduction\n\nEnd-to-end machine learning projects require long-term lifecycles during which different models are evaluated,\nwith various hyperparameters or data representations. \nThen, out of all the experiments, a final model must be selected for deployment in the production environment.\nThere are some solutions available to manage the model creation process, such as [mlflow](https://mlflow.org/)\nor [neptune.ai](https://neptune.ai/). However, none of them support the functionality of monitoring a deployed model in production.\n\nAs a part of the mlops project, we aim to create a ready-to-use tool for professionals in the Machine Learning industry \nallowing them not only to **manage experiments during model creation process (tracking module)**, \nbut also **monitoring a deployed model working on real-world production data (monitoring module)** \nwith an option to **setup email alerts using [MailGun](https://www.mailgun.com/) (email alerts module)**.\n\n## Explanatory video\n[![mlops-ai explanatory video](https://img.youtube.com/vi/eM1tSxPxrsU/maxresdefault.jpg)](https://www.youtube.com/watch?v=eM1tSxPxrsU)\n\n## Installation \u0026 usage\n\nTo install the application locally, you need to have [docker](https://docs.docker.com/get-docker/) and \n[docker-compose](https://docs.docker.com/compose/install/) installed on your machine. \nThen, you can run the following command:\n\n```bash\ndocker-compose up\n```\n\nAfter that you can access the application at [http://localhost:3000](http://localhost:3000).\n\n\nTo install the python package make sure you have [Python \u003e=3.9](https://www.python.org/downloads/) installed on your machine.\nThen, you can install the package using pip:\n\n```bash\npip install mlops-ai\n```\n## Technologies\n\nApplication consist of two main components:\n- Main application (client + server) written in [React](https://reactjs.org/) and [FastAPI](https://fastapi.tiangolo.com/), \nwhich you can run using [Docker](https://www.docker.com/).\n- [Python package](https://pypi.org/project/mlops-ai/) for communication with the application.\n\nAdditionally, we use [mongoDB](https://www.mongodb.com/) database for storing tracking module data.\n\n## Documentation\n\nYou can find the detailed documentation of the application [here](https://mlops-ai.github.io/mlops/).\n\n## Examples\n\nThe main end-to-end notebook that \npresents key features of the package can be found \n[here](https://github.com/mlops-ai/mlops/blob/develop/library/tests/notebooks/mlops-ai-library-showcase.ipynb).\nSome other example notebooks are also provided inside the `library/tests/notebooks` directory. \n\n## License\n\nDistributed under the open-source Apache 2.0 License. See `LICENSE` for more information.\n\n\n## Contact\n\nProject authors are (in alphabetical order):\n- [Paweł Łączkowski (front-end)](https://github.com/dzikafoczka)\n- [Kacper Pękalski (back-end, library)](https://github.com/kacperxxx)\n- [Jędrzej Rybczyński (back-end, library)](https://github.com/directtt)\n- [Kajetan Szal(back-end, library)](https://github.com/kajetsz/)\n\nFeel free to contact us in case of any questions or suggestions.\n\n## References\n\nThis project was created as a final BE project of Computer Science course at\n[Faculty of Mathematics and Computer Science](https://wmi.amu.edu.pl/en) \nof [Adam Mickiewicz University](https://amu.edu.pl/en). \n\n## To-Do\n\nApplication is still under development.\nHere is a list of features we plan to implement in the future:\n- [x] Add support for the whole monitoring module\n- [x] Add support for email alerts\n- [x] AWS EC2 integration\n- [ ] Add support for multiple users (optionally)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlops-ai%2Fmlops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmlops-ai%2Fmlops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlops-ai%2Fmlops/lists"}