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
Building a Docker Image for the Flask App
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docker build, Docker Documentation, 2024 - Provides the official and authoritative reference for thedocker buildcommand, including its syntax, options like-tfor tagging, and handling of the build context, which are essential aspects covered in the section.- Best practices for writing Dockerfiles, Docker Documentation, 2024 - Offers official guidelines and recommendations for creating efficient, secure, and maintainable Dockerfiles. While the section focuses on building an image from an existing Dockerfile, understanding these practices improves the quality and efficiency of the resulting Docker images.
- Docker in Action, Jeff Nickoloff, Stephen Kuenzli, 2019 (Manning Publications) - A comprehensive book that offers practical, real-world examples of using Docker. It covers building images, working with Dockerfiles, and managing applications in containers, offering a broader and more detailed understanding of the Docker ecosystem than specific command references.
- Designing Machine Learning Systems: An Iterative Approach, Chip Huyen, 2022 (O'Reilly Media) - This book addresses the practical aspects of building and deploying machine learning systems, including the role of containerization. It provides valuable context for why Docker image building is a foundational step in deploying machine learning models consistently and reliably.
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