Deploying PyTorch Models

Introduction

Welcome to our tutorial on deploying models with PyTorch! In this guide, you will learn how to take your trained models from development to production seamlessly.

Why Deploy Models?

Deploying models enables you to put your AI applications into production, allowing them to serve predictions to real-world users. This is crucial for applications ranging from autonomous cars to financial forecasting.

Steps to Deploy a Model

  1. Prepare your environment: Ensure all necessary libraries and tools are installed.
  2. Serialize your model: Use torch.save() to save your trained model.
  3. Set up a server: Consider using Flask or FastAPI for building APIs.
  4. Test and monitor: Continuously evaluate the performance of your deployed model.

Useful Resources