Deployment is a crucial move in the ML workflow. It is a mark where we want to implement our ML model into utilization. Later, we can practice the model in practical life.
But how can we design the model as a treatment? We can develop an Application Programming Interface (API). With that, we can reach the model universally, can be a mobile application or web application. In Python, there’s a library that can assist us in building an API. It’s named Flask.
This article will explain how to construct a REST API for our machine learning model utilizing Flask. Without further ado, let’s begun!
Flask is the first step, but then I’d want to reverse proxy it with gunicorn or Nginx afterward.