Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart.

In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. You’ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis.

Learn the challenges and requirements of an ML system in production
Build training data with different sampling and labeling methods
Leverage best techniques to engineer features for your ML models to avoid data leakage
Select, develop, debug, and evaluate ML models that are best suit for your tasks
Deploy different types of ML systems for different hardware
Explore major infrastructural choices and hardware designs
Understand the human side of ML, including integrating ML into business, user experience, and team structure

.
http://www.rarefile.net/r9544w73fupl/DesigningMachine.LearningSystemsFinal.zip
.