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
Introduction to Model Serialization
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pickle- Python object serialization, Python Software Foundation, 2024 - Official documentation for Python's built-in serialization module, explaining its functionality and usage for persistent storage of objects.joblib- Running Python functions as pipeline jobs, `joblib` developers, 2024 - Official documentation for thejobliblibrary, highlighting its efficiency for large numerical arrays and its application in machine learning model persistence.- Persistence, Scikit-learn developers, 2024 - A practical guide from the widely used Scikit-learn machine learning library on how to save and load models, typically recommending
jobliborpickle. - Machine Learning Design Patterns, Valliappa Lakshmanan, Sara Robinson, and Michael Munn, 2020 (O'Reilly Media) - This book presents design patterns for building reliable and scalable machine learning systems, including methods for model deployment and serialization.