Machine Learning Engineering

by Andriy Burkov

Covers the complete lifecycle of production ML systems, including best practices for monitoring, maintenance, fallback strategies, handling adversaries, and all the practical challenges that arise when deploying machine learning at scale.

• Introduction
• Before the Project Starts
• Data Collection and Preparation
• Feature Engineering
• Supervised Model Training (Part 1)
• Supervised Model Training (Part 2)
• Model Evaluation
• Model Deployment
• Model Serving, Monitoring, and Maintenance
• Conclusion