Introduction to Machine Learning Deployment
Chapter 1: Getting Started with Model Deployment
What is Machine Learning Deployment?
Why Deploy Machine Learning Models?
The Machine Learning Workflow Overview
Types of Deployment Strategies (Introduction)
Challenges in Model Deployment
Quiz for Chapter 1
Chapter 2: Preparing Your Model for Deployment
Saving Trained Models
Introduction to Model Serialization
Using Pickle for Model Persistence
Using Joblib for Model Persistence
Handling Model Dependencies
Saving Preprocessing Steps
Hands-on Practical: Saving and Loading a Simple Model
Quiz for Chapter 2
Chapter 3: Creating a Prediction Service with Flask
What is an API?
Introduction to Web Frameworks
Setting Up Flask
Creating a Basic Flask Application
Loading Your Saved Model in Flask
Defining a Prediction Endpoint
Handling Input Data (JSON)
Returning Predictions
Testing Your API Locally
Hands-on Practical: Building a Simple Flask Prediction API
Quiz for Chapter 3
Chapter 4: Introduction to Containerization with Docker
What is Containerization?
Introduction to Docker
Docker Concepts: Images and Containers
Installing Docker
Writing a Simple Dockerfile
Building a Docker Image for the Flask App
Running the Application in a Docker Container
Hands-on Practical: Containerizing the Prediction Service
Quiz for Chapter 4
Saving Trained Models
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- Save and load Keras models, Neel Kovelamudi, Francois Chollet, 2023 - The official TensorFlow guide detailing the methods for persisting Keras models using TensorFlow's native SavedModel format and the H5 format, which is important for deep learning model deployment.
- Saving and Loading Models, Matthew Inkawhich, 2018 (PyTorch Foundation) - Official PyTorch tutorial that explains the methods for saving and loading PyTorch models, which is essential for deep learning model persistence.