How to Create a Simple Flask Web App for Hosting a Machine Learning Prediction API
This video provides an overview of how to use Flask (a micro web framework) to create an API for clients to get predictions from a deployed decision tree machine learning model using Python. Flask routes, view functions, request arguments (query string parsing), and request responses are covered.
The decision tree used is based on a toy "interview" dataset that I don't have a reference for (sorry! let me know in the comments if you know its original source). The tree was trained using an entropy-based TDIDT (top-down induction of decision trees) algorithm that is not covered in this video.
This video is part 3 of an 8 part series on APIs and machine learning model deployment with Python. The next video in this series is: • How to use Pickling to Save a Trained Mach...
The previous video in this series is: • How to Make GET Requests to Web APIs using...