[DRAFT] Real-Time Machine Learning: A Practitioner’s Guide, with Examples from Fintech
Hello!
Welcome to Real-Time Machine Learning: A Practitioner’s Guide. This book provides practical guidance for building and maintaining real-time machine learning systems in production.
About This Book
This book covers the entire lifecycle of real-time ML systems, from initial project planning to steady-state operations and scaling.
The scope of this book is ML systems in reasonable-scale organizations
Examples from Fintech
I firmly believe that the best way to teach is by leading with simple examples to quickly convey the big picture and then add an explanation, following up with more advanced examples.
Fintech organizations, especially banks, have been using ML models for a long time so there are many interesting and complex problems to see, which allows us to explore the many dimensions and trade-offs involved in using real-time ML.
Patterns and Lessons Learned
In addition to examples, patterns and lessons learned are two ways I found to convey experience from operating real-time ML systems. These come from my own learnings and from the literature at large.
Patterns: things that are self-contained and common enough to merit a short “name”. Examples: shadow-mode deployment, pre-mortem, walking skeleton.
Lessons Learned: These are short sentences with statements of fact, suggestions or warnings about a particular aspect of real-time ML.
What this book is not
This is not an introductory book to ML. It assumes the reader already understands what ML is and how it’s used.
This book is not aimed at teaching you how to use a particular tool or framework. It is focused on high-level concepts and ideas instead.
Is there a PDF version of the book?
Yes. Every new release triggers a new PDF version. Download the latest PDF here.