Make US Rich 💰
Welcome, you are at the right place to learn more about the Make US Rich project. This project is a tool to help people to train, serve and use cryptocurrencies forecasting models.
This is you counting money after deploying the project
This project was build by @ChainYo to help people building their own MLOps projects.
Warning
Cryptocurrencies are only a pretext to build a machine learning project. You won't be able to use this project to make real money, but you can use it to train, serve and use your own models.
In fact, you can use the project baselines to train, serve and use any kind of machine learning models.
We will see how to use the project in the following sections. Please feel free to ask questions and share your ideas by opening an issue. You help and opinion is welcome in order to improve the project.
Prerequisites
You need to have a Python 3.8+ environment to run this project. I personally use Miniconda for this purpose.
Combined with poetry, it seems to be a good way to manage dependencies and running the project in an isolated
python environment.
The project also requires docker and docker-compose to be installed. We will use them to run the serving and interface
part of the project.
To install docker and docker-compose follow the instructions on the Docker documentation.
Installation
Create an isolated python environment with conda:
conda create -y -n make-us-rich python=3.8
Activate the environment and install the make-us-rich package:
conda activate make-us-rich
poetry add make-us-rich
If you are using pip, you can use the following command instead
pip install make-us-rich
After everything is installed, you can run any component of the project.
Architecture of the project
The project is composed of three components: interface, serving and training.
Each component has its own folder and its own specific configuration. All details about each component are explained in their respective documentation.
Here is a simple diagram of the project:
🚀 Let's dive into the components details!
Tip
You can start with any, but I recommend to start with the training component.