Python: Machine Learning Model Deployment

This Python snippet demonstrates how to deploy a trained machine learning model for real-time predictions using the 'flask' framework. It's designed for data scientists and developers who want to make their machine learning models accessible via a web interface.

The snippet sets up a simple web server with Flask that can receive input data through an API, apply the model to the data, and return predictions. This deployment method is crucial for integrating machine learning capabilities into business applications.

Using 'flask' to create the API endpoint and handle requests, the code serves the machine learning model's predictions to end-users or client applications, facilitating the practical use of machine learning insights.

This tool is invaluable for companies leveraging machine learning to enhance decision-making processes, optimize operations, or provide enhanced customer experiences.

Below is the complete code for deploying a machine learning model with Flask, enabling real-time predictions and broader application integration.