How To Deploy ML Models as a Service Using Flask

Author(s): Richard Warepam

Originally published on Towards AI.

4 Steps: ML Model -> API Service -> Access from Anywhere

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Let’s talk about machine learning models as a service today. If you’re wondering, “What is ML as a service, though?” You’re at the right place.

This will be a very simple guide, designed to help you understand the flow of operations for any ML model as a service.

Let me paint you a picture: When you use any AI Tools, you have no idea what is going on in the background, and all you have to do is click the “Generate” button and taaaddaaaa! you get the result.

So, this is a type of ML model as a service. It simply implies that you are building a service that abstracts an ML model from the users.

Technically, the term “ML as a service” means “to run an ML model as a service where the model is wrapped in a program/API so that we can invoke the model functions from anywhere on the internet through HTTP protocols by anyone (users or other applications)”.

You got the basic idea, right?

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Published via Towards AI