There’s a lot of difference between building a machine learning model and deploying it to production to see how it works, as the two jobs differ in both profession and skill set. But in this article, I’ll walk you through how to deploy a machine learning model with Python.
A machine learning engineer or a data scientist is the one who creates a machine learning model and a software developer or a web developer is a person responsible for deploying the machine learning model. But in this article, I’ll show you how to deploy a machine learning model using Python, HTML, and CSS.
Also, Read – 100+ Machine Learning Projects Solved and Explained.
How To Deploy a Machine Learning Model?
When working on a machine learning task, as a data scientist or machine learning engineer, we have to focus a lot on exploratory data analysis, data preparation, feature engineering, tuning of hyper-parameters and model selection. In the process, we only focus on improving the accuracy of the model, but we forget the main objective which is to extract some values generated from the machine learning model in real-time, which cannot be done without deploying it to a user interface-based system.
Deploying a machine learning model means preparing it for end-user use. To deploy machine learning models, we are faced with such issues that not all data scientists are familiar with languages such as HTML and CSS. But in the section below, I’ll walk you through how to easily deploy a machine learning model using Python, HTML, and CSS.
Deploy a Machine Learning Model with Python
Here I will deploy a machine learning model using Python, HTML, and CSS. I will first train a machine learning model for the task of SMS Spam detection. The task of SMS spam detection model is to classify spam and not spam messages.
At the end of this article, you will be able to copy and paste your messages in the user interface and the application will show you whether it’s spam or not. Now let’s see how to train and deploy this machine learning model using Python, HTML and CSS.
To Deploy a model using Python, HTML and CSS we need 4 files, namely:
- App.py: The driver code, which will consist of the code to train a machine learning model and creating a flask API.
- home.html: which will be a landing page where we will deploy our model.
- style.css: which will be used to design our landing page
- result.html: which will show us the result whether the message is spam or not.
Now let’s train and deploy our model using Python, HTML and CSS. Let’s start by creating the first file that is app.py. You can download the dataset that I am using from below. Before I start writing code make sure that you create all these folders as mentioned below and add the home.html and result.html files in the templates folder. Likewise, you need to add the style.css file in the static folder.
Now let’s see how to train and deploy machine learning models with Python, HTML and CSS:
app.py:
home.html:
style.css:
result.html:
Output:
You will get a link in the output, click on the link and you will get to see the output:
I hope you liked this article on how to deploy a machine learning model using Python, HTML and CSS. Feel free to ask your valuable questions in the comments section below.
