An End to End machine learning model follows the complete lifecycle of a machine learning model which means to first collect data, then prepare the data according to the patterns found in the dataset, then train and evaluate the model and at last deploy the model. So an end to end machine learning project means to present a model in the form of an interactive application that runs the model on the dataset and produces output in the realtime according to the user input. In this article, I will take you through an end to end machine learning project using Python.
End to End Machine Learning Project
An end to end machine learning project means to create an interactive application that runs our trained machine learning model and give output according to the user input. It follows the complete lifecycle of a machine learning model. Below are the steps that you need to follow while creating an end to end application for your model:
- Data access and collection
- Data preparation and exploration
- Model training and evaluation
- Model deployment
Creating an end to end machine learning application is important to show most of your skills in a single project. In the section below, I will take you through how to create an end to end machine learning application using Python.
End to End Machine Learning Project using Python
I will be using the streamlit framework in python to create a web interface for interacting with the machine learning model. Here I will deploy a text emotion prediction model which I presented recently in one of the previous articles. You can learn how to train a model for the task of text emotion prediction from here. Below is the complete code to present this machine learning model in the form of an interactive web interface:
As you are using the streamlit framework here so you have to run this file by using the command streamlit run filename.py. After running this file you will see a web interface that will directly open in your default browser and you will see an output like this:
So as you can see a user input in the output, simply write a text to predict the emotion of that text and hit enter. The interface will take the same time to run as the time taken by your Python file. After executing the model on the user input it will print the emotion of the text entered by the user. This is how you can create an interactive interface for your machine learning model.
In this article, I introduced you to how to build an interactive web interface to create an end-to-end machine learning application. The streamlit framework offers a lot of features to make your web interface more interactive and user friendly that you can learn from the official documentation here. I hope you liked this article om how to create an end to end machine learning model using Python. Feel free to ask your valuable questions in the comments section below.