"This book is different. The author goes way beyond the theories and models to help simplify the complexities in building, measuring and managing complete large scale intelligence systems."
-Customer ReviewPaperback Audio Book
This course is aimed at software engineers who want to understand the specific challenges of working with AI components and at data scientists who want to understand the challenges of getting a prototype model into production; it facilitates communication and collaboration between both roles.Learn More
This course is designed as a first exposure to Machine Learning for engineers and program mangers. Upon completion students will be prepared to build machine learning systems in practice. The course will cover: key algorithms in detail, practical modeling theory & practice, and machine learning engineering.Learn More
In this course, we will learn the fundamental differences between ML/AI as a technique versus ML/AI as a system in production. A ML/AI system involves a significant number of components and it is important that they remain responsive in the face of failure and changes in load. This course covers several strategies to keep ML/AI systems responsive, resilient, and elastic.Learn More
"This is the first Systems Engineering text which gives the success factors for creating an Internet or Major Engineering project to deliver human experiences based upon AI and Machine Learning. This book is immensely practical and necessary."
"If you're looking to understand how to build & ship intelligence into your products, this book will give you a roadmap to do that. I highly recommend for PMs and engineering managers especially."
"This book explains ... with lots of concrete examples and very clear explanations about each and every step required to deliver a successful intelligent experience. ... I also really liked the tone of the book, full of funny examples and witty remarks."
All the concepts you need to succeed with Machine Learning in practice.
What they are, what they are good for, and how to set one up for success.
Achieve your goals, mitigate mistakes, and produce data to improve over time.
Key concetps for how to excute, manage, and measure Intelligent Systems in practice.
Reliably use a variety of approaches, particularly machine learning.
Bring the parts together throughout your system's life cycle to achieve the impact you want.
Building Intelligent Systems is about Machine Learning Engineering. It does not teach machine learning algorithms or machine learning math. Here are some great references for those topics: