After training and evaluating a machine learning model, the next step is to deploy the model. As a Data Scientist, you don’t need to build a complete software or web application to deploy your machine learning models, but you still need to know how your models should be deployed, which can be done by testing your model on an interactive application. In this article, I will introduce you to the best ways to deploy your machine learning models.
How To Deploy Machine Learning Models?
There are so many tools that offer you to test your machine learning models by deploying them. If you use Python then you have a lot of libraries and frameworks for this task, but I will be covering only the best ones here. So 3 most popular tools allow you to deploy machine learning models:
- Streamlit
- Flask
- FaaS platforms
Let’s go through all these tools one by one to understand how to build and deploy any machine learning model.
Streamlit
Streamlit is an open-source Python library that offers you to deploy and share your machine learning models in the form of web applications. In just a few lines of code, you can see yourself interacting and executing your code in a web application.
If you don’t like to create complete web applications or you don’t want to spend much time deploying your machine learning models then using streamlit is the best option for you. Below is a tutorial that will help you to create your first application using streamlit.
Flask
Flask is also a great framework to deploy a machine learning model but you have to be good in web development while using Flask. While creating an application using Flask you also have to use HTML and CSS to give a good appearance to your application.
Creating applications is not as easy in Flask as in Streamlit but both these Python frameworks are best in their way. The only difference is that Streamlit is easy and a better option in my opinion.
FaaS Platforms
FaaS stands for Functions as a service. These platforms are used for the fully managed deployment of machine learning models as HTTP endpoints. One of the popular examples of FaaS includes services like IBM Cloud, Amazon AWS, Google Cloud, Microsoft Azure, etc.
So these were the best ways to deploy a machine learning model. I hope you liked this article on how to deploy machine learning models. Feel free to ask your valuable questions in the comments section below.
