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
What is an API?
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- RESTful Web APIs, Leonard Richardson, Mike Amundsen, Sam Ruby, 2013 (O'Reilly Media) - A foundational guide to understanding the REST architectural style, web API design, and HTTP interactions.
- Flask Documentation, Pallets, 2024 - The official and current documentation for the Flask web framework, detailing its features and API development.
- The JSON Data Interchange Format, T. Bray, Ed., 2017 IETF RFC 8259 (Internet Engineering Task Force (IETF)) - The formal specification for JSON (JavaScript Object Notation), which is a common data format for web APIs.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready ML Applications, Chip Huyen, 2022 (O'Reilly Media) - Covers the process of deploying machine learning models and the role of APIs in serving predictions to applications.