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 Machine Learning Deployment?
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - This authoritative book offers a practical and in-depth exploration of designing and building machine learning systems for production, covering the entire lifecycle from data to deployment and monitoring.
- Practitioners Guide to MLOps: A Framework for Continuous Delivery and Automation of Machine Learning, Google Cloud, 2021 (Google Cloud) - This official guide from Google Cloud provides a structured framework and best practices for implementing MLOps, detailing the critical steps for continuous integration, continuous delivery, and continuous training of ML models, including deployment strategies.
- MLOps: A Survey, Current Challenges, and Future Directions, Tobias Kreuzberger, Nils Kühl, Sebastian Hirschl, 2023 ACM Computing Surveys, Vol. 56 (ACM) DOI: 10.1145/3607068 - This comprehensive academic survey synthesizes the current state of MLOps, defining its core concepts, including machine learning deployment, and discusses contemporary challenges and future research directions in the field.