Develop and sell a Machine Learning app — from start to end tutorial
COVID-19 prediction end-to-end app
After developing and selling a Python API, I now want to expand the idea with a machine learning solution. So I decided to quickly write a COVID-19 prediction algorithm, deploy it, and make it sellable. If you want to see how I did it, check out the post for a step by step tutorial.
Table of Contents
- About this article
- Stack used
- 1. Create project formalities
- 2. Develop a solution for a problem
- Install packages and track jupyter files properly
- Develop solution to problem
- Build server to execute function with REST
- BONUS: Make reproducible with Docker
- 3. Deploy to AWS
- Set up zappa
- Set up AWS
- 4. Set up Rapidapi
- End result
- Final links
About this article
In this article, I take the ideas from my previous article “How to sell a Python API from start to end” further and build a machine learning application. If the steps described here are too rough consider reading my previous article first.
There are a number of new and more complicated issues to cover in this project:
- Machine Learning content. The application takes basic steps of building a Machine Learning model. This covers the preparation, but also the prediction.
- In time evaluation (not in time training) of the prediction. This means that the dataset is freshly fetched and the prediction is performed on the latest data.
- Deployment. Deploying a Machine Learning app has various challenges. In this article, we met and solved the issue of outsourcing the trained model on AWS.
- It is not only an API but also has a minor frontend.
It paints a picture for developing a Python API from start to finish and provides help in more difficult areas like the setup with AWS Lambda.
There were various difficulties, which allowed me to learn more about the deployment and building process. It is also a great way to build side projects and maybe even make some money.
Click on the link below for full report