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
Types of Deployment Strategies (Introduction)
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready ML Applications, Chip Huyen, 2022 (O'Reilly Media) - This book offers a comprehensive guide to building and deploying machine learning systems, with dedicated sections on model serving strategies, including batch, online, and streaming inference.
- Machine Learning Engineering, Andriy Burkov, 2020 (True Positive Inc.) - A practical book addressing the engineering aspects of putting machine learning models into production, covering topics like model serving and different deployment environments.
- Batch prediction vs. online prediction, Google Cloud, 2024 (Google Cloud) - Official documentation explaining the distinctions, use cases, and typical workflows for batch and online prediction services, providing a clear industry perspective on these deployment strategies.