https://github.com/mlops-ai/mlops
Open-source tool for tracking & monitoring machine learning models.
https://github.com/mlops-ai/mlops
ai data-science machine-learning mlflow mlops neptune python
Last synced: 6 months ago
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Open-source tool for tracking & monitoring machine learning models.
- Host: GitHub
- URL: https://github.com/mlops-ai/mlops
- Owner: mlops-ai
- License: apache-2.0
- Created: 2023-04-19T14:54:02.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-06T21:22:23.000Z (over 1 year ago)
- Last Synced: 2025-09-24T23:54:25.039Z (9 months ago)
- Topics: ai, data-science, machine-learning, mlflow, mlops, neptune, python
- Language: TypeScript
- Homepage: https://pypi.org/project/mlops-ai/
- Size: 96.3 MB
- Stars: 23
- Watchers: 4
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mlops
Open-source tool for **tracking** & **monitoring** machine learning models.
[](https://badge.fury.io/py/mlops-ai)
[](https://opensource.org/licenses/Apache-2.0)
## Table of Contents
- [Introduction](#introduction)
- [Explanatory video](#explanatory-video)
- [Installation & usage](#installation--usage)
- [Technologies](#technologies)
- [Documentation](#documentation)
- [Examples](#examples)
- [License](#license)
- [Contact](#contact)
- [References](#references)
- [To-Do](#to-do)
## Introduction
End-to-end machine learning projects require long-term lifecycles during which different models are evaluated,
with various hyperparameters or data representations.
Then, out of all the experiments, a final model must be selected for deployment in the production environment.
There are some solutions available to manage the model creation process, such as [mlflow](https://mlflow.org/)
or [neptune.ai](https://neptune.ai/). However, none of them support the functionality of monitoring a deployed model in production.
As a part of the mlops project, we aim to create a ready-to-use tool for professionals in the Machine Learning industry
allowing them not only to **manage experiments during model creation process (tracking module)**,
but also **monitoring a deployed model working on real-world production data (monitoring module)**
with an option to **setup email alerts using [MailGun](https://www.mailgun.com/) (email alerts module)**.
## Explanatory video
[](https://www.youtube.com/watch?v=eM1tSxPxrsU)
## Installation & usage
To install the application locally, you need to have [docker](https://docs.docker.com/get-docker/) and
[docker-compose](https://docs.docker.com/compose/install/) installed on your machine.
Then, you can run the following command:
```bash
docker-compose up
```
After that you can access the application at [http://localhost:3000](http://localhost:3000).
To install the python package make sure you have [Python >=3.9](https://www.python.org/downloads/) installed on your machine.
Then, you can install the package using pip:
```bash
pip install mlops-ai
```
## Technologies
Application consist of two main components:
- Main application (client + server) written in [React](https://reactjs.org/) and [FastAPI](https://fastapi.tiangolo.com/),
which you can run using [Docker](https://www.docker.com/).
- [Python package](https://pypi.org/project/mlops-ai/) for communication with the application.
Additionally, we use [mongoDB](https://www.mongodb.com/) database for storing tracking module data.
## Documentation
You can find the detailed documentation of the application [here](https://mlops-ai.github.io/mlops/).
## Examples
The main end-to-end notebook that
presents key features of the package can be found
[here](https://github.com/mlops-ai/mlops/blob/develop/library/tests/notebooks/mlops-ai-library-showcase.ipynb).
Some other example notebooks are also provided inside the `library/tests/notebooks` directory.
## License
Distributed under the open-source Apache 2.0 License. See `LICENSE` for more information.
## Contact
Project authors are (in alphabetical order):
- [Paweł Łączkowski (front-end)](https://github.com/dzikafoczka)
- [Kacper Pękalski (back-end, library)](https://github.com/kacperxxx)
- [Jędrzej Rybczyński (back-end, library)](https://github.com/directtt)
- [Kajetan Szal(back-end, library)](https://github.com/kajetsz/)
Feel free to contact us in case of any questions or suggestions.
## References
This project was created as a final BE project of Computer Science course at
[Faculty of Mathematics and Computer Science](https://wmi.amu.edu.pl/en)
of [Adam Mickiewicz University](https://amu.edu.pl/en).
## To-Do
Application is still under development.
Here is a list of features we plan to implement in the future:
- [x] Add support for the whole monitoring module
- [x] Add support for email alerts
- [x] AWS EC2 integration
- [ ] Add support for multiple users (optionally)