Overview:

This document provides instructions on creating a Machine Learning API using Flask. The code accompanying the AnalyticsVidhya article is discussed, along with a disclaimer that the code is a bit old and not recommended for production level tasks. The document suggests using FlaskAppBuilder, Quart, or FastAPI for production APIs. It also includes guidance on setting up the Anaconda environment for the project.

Features:

Machine Learning API: Create a Machine Learning API using Flask.
Code Accompanying AnalyticsVidhya Article: Discusses the code associated with an article on AnalyticsVidhya.
Anaconda Environment Setup: Provides instructions for setting up the Anaconda environment for the project.
Virtual Environment Creation: Guides on creating a virtual environment using the provided yml file.
Compatibility Control: Warns against using the provided code for production tasks due to its age and suggests using modern frameworks.

Summary:

The document provides a guide on creating a Machine Learning API using Flask and discusses the code related to an AnalyticsVidhya article. It emphasizes that the code is outdated and not suitable for production tasks, suggesting the use of FlaskAppBuilder, Quart, or FastAPI for production APIs. Furthermore, it offers instructions on setting up the Anaconda environment for the project, ensuring the successful creation of a virtual environment.

Pratos

Flask_api