Deploying Your Machine Learning Model as a REST API Using Flask

Deploying Your Machine Learning Model as a REST API Using Flask

In this tutorial, we'll learn Deploying Your Machine Learning Model as a REST API Using Flask

There are thousands of online courses which teach you how to build and train a machine learning model or deep neural network, many of these tutorials end with you just training and building the model alone. This, however, ends you having a model file on your local computer, which isn’t the end of the story: the model needs to be deployed in order to be used by the clients or end-users.

As a Web developer and data scientist, I have a desire to build web apps to showcase my work. As much as I like to design the front-end, it becomes very overwhelming to take both machine learning and web development. So, I had to find a solution that could easily integrate my machine learning models with other developers who could build a robust web app better than I can.

By building a REST API for my model, I could keep my code separate from other developers. There is a clear division of labour here which is nice for defining responsibilities and prevents me from directly blocking teammates who are not involved with the machine learning aspect of the project. Another advantage is that my model can be used by multiple developers working on different platforms, such as web or mobile.

machine-learning flask deployment web-development rest-api

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