Learn the fundamental concepts and techniques for deploying machine learning models. This course covers preparing models for production, creating simple prediction services, and understanding basic deployment patterns, enabling you to make your trained models accessible and useful.
Prerequisites Python and ML Basics
Level:
Model Deployment Concepts
Understand why model deployment is necessary and its role in the machine learning lifecycle.
Model Preparation
Learn how to save and load trained machine learning models using common serialization techniques.
API Development Basics
Understand the concept of an API and how it's used for model serving.
Simple Web Service Creation
Build a basic web service using Flask to serve predictions from a trained model.
Containerization Introduction
Grasp the fundamentals of containerization with Docker for packaging applications.