🚀 Day 76 of #100DaysOfCode in Python: Mastering Machine Learning Deployment

Photo by Efe Kurnaz on Unsplash

Welcome to Day 76! Today, we’re focusing on a crucial aspect of the machine learning lifecycle: deploying your model. Deployment is the process of integrating a machine learning model into an existing production environment to make predictions based on new data. It’s a critical step to share your AI models with the world effectively.

1. Understanding ML Deployment

• Objective: The goal is to make your trained model accessible to users, applications, or other services.
• Challenges: Includes ensuring model performance, scalability, maintainability, and security.

2. Model Serialization

Before deployment, you need to save or serialize your trained model. In Python, libraries like pickle or joblib are commonly used for this purpose.

import joblib
# Save the model
joblib.dump(trained_model, 'model.pkl')

# Load the model
model = joblib.load('model.pkl')

3. Creating a Prediction API

An API (Application Programming Interface) acts as a bridge between your model and users or applications.

• Flask: A lightweight WSGI web application framework that can be used to create APIs…