You’ll find plenty of machine learning projects and tutorials on the internet, but only a few of them focus on end-to-end model deployment. So, if you want to learn how to build an end-to-end machine learning model, then this article is for you. In this article, I’ll walk you through how to build an end-to-end machine learning model using Python.
How to Build an End-to-End Machine Learning Model?
An end-to-end machine learning model means that you train a model and deploy it into an end product or application where it can be used to add value to an organization. For example, when you train a model for the task of predicting stock prices, it cannot be said to be an end-to-end model. But if you build an app where you give a user input as the name of the stock or its symbol and get its future prices as output, this is an end-to-end model.
So we can say that if you build an application where you can test the predictions of your trained model, it is an end-to-end model. This is why having an end to end application in your data science resume strengthens your profile as it shows that you know how to train a model that can add value to an organization when deployed in the product or the final application. In the section below, I’ll walk you through how to build an end-to-end model using Python.
End-to-End Machine Learning Model using Python
We can use several ways in Python to build an end-to-end application for your model. I mainly use the streamlit library in Python which is so easy to use that it can deploy your model into an application in a few lines of code. If you’ve never used it before, you can easily install it using the pip command:
- pip install streamlit
Now I will walk you through how to create an end-to-end model for the task of future price prediction. Here I am going to deploy a dogecoin price prediction model that I trained before. You can find it from here. Now let’s see how to deploy a model in an end-to-end application:
To run this code, you have to write this command in your command prompt or terminal:
- streamlit run filename.py
And then you will see a web page open in your default browser that will display your final output as shown below:
So this is how to create an end-to-end application for your trained model. You can find some more end-to-end machine learning projects solved and explained using Python from here. Hope you liked this article on how to build an end-to-end application for your machine learning models. Please feel free to ask your valuable questions in the comments section below.