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
The Machine Learning Workflow Overview
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive guide to the entire machine learning system lifecycle, from problem framing and data engineering to model deployment and monitoring in production environments.
- Rules of Machine Learning: Best Practices for ML Engineering, Martin Zinkevich, 2017 (Google) - Offers practical advice and best practices from Google for building and deploying machine learning systems, addressing various stages of the workflow.
- Introducing MLOps: How to go from Model-centric to Data-centric AI, Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, 2020 (O'Reilly Media, Inc.) - Introduces the principles and practices of MLOps, focusing on operationalizing machine learning models and managing their lifecycle in production.