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About this book

Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success.

This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.

Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world.

What You’ll Learn

Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for successDesign an intelligent user experience: Produce data to help make the Intelligent System better over timeImplement an Intelligent System: Execute, manage, and measure Intelligent Systems in practiceCreate intelligence: Use different approaches, including machine learningOrchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want

Who This Book Is For

Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems

Table of Contents

Frontmatter

Approaching an Intelligent Systems Project

Frontmatter

Chapter 1. Introducing Intelligent Systems

Abstract
Intelligent Systems are all around us. In our light bulbs. In our cars. In our watches. In our thermostats. In our computers. How do we make them do the things that make our lives better? That delight us?
Geoff Hulten

Chapter 2. Knowing When to Use Intelligent Systems

Abstract
So you have a problem you want to solve. An existing system you need to optimize. A new idea to create a business. Something you think your customers will love. A machine-learning–based solution that isn’t producing the value you’d hoped. Is an Intelligent System right for you? Sometimes yes. Sometimes no.
Geoff Hulten

Chapter 3. A Brief Refresher on Working with Data

Abstract
Data is central to every Intelligent System. This chapter gives a conceptual overview of working with data, introducing key concepts from data science, statistics, and machine learning. The goal is to establish a baseline of understanding to facilitate discussions and decision making between all participants of an Intelligent System project.
Geoff Hulten

Chapter 4. Defining the Intelligent System’s Goals

Abstract
An Intelligence System connects intelligence with experience to achieve a desired outcome. Success comes when all of these elements are aligned: the outcome is achievable; the intelligence is targeted at the right problem; and the experience encourages the correct user behavior.
Geoff Hulten

Intelligent Experiences

Frontmatter

Chapter 5. The Components of Intelligent Experiences

Abstract
At the core of every Intelligent System is a connection between the intelligence and the user. This connection is called the intelligent experience. An effective intelligent experience will
Geoff Hulten

Chapter 6. Why Creating Intelligent Experiences Is Hard

Abstract
This chapter explores some ways that experiences based on intelligence are different from more traditional experiences. And here is the bottom line: intelligence makes mistakes.
Geoff Hulten

Chapter 7. Balancing Intelligent Experiences

Abstract
Designing a successful intelligent experience is a balancing act between
Geoff Hulten

Chapter 8. Modes of Intelligent Interaction

Abstract
There are many, many ways to create user experiences, and just about all of them can be made intelligent. This chapter explores some broad approaches to interaction between intelligence and users and discusses how these approaches can be used to create well-balanced intelligent experiences. These approaches include
Geoff Hulten

Chapter 9. Getting Data from Experience

Abstract
Intelligence creators can work with all sorts of data, even crummy data. But when they have good data, their job is much easier—and the potential of the intelligence they can create is much greater. An ideal intelligent experience will control the interactions between users and the Intelligent System so that the record of those interactions makes it easy to create high-quality intelligence.
Geoff Hulten

Chapter 10. Verifying Intelligent Experiences

Abstract
Intelligent experiences fail for two main reasons
Geoff Hulten

Implementing Intelligence

Frontmatter

Chapter 11. The Components of an Intelligence Implementation

Abstract
In order to have impact, intelligence must be connected to the user.
Geoff Hulten

Chapter 12. The Intelligence Runtime

Abstract
The intelligence runtime puts intelligence into action. It is responsible for interfacing with the rest of the Intelligent System, gathering the information needed to execute the system’s intelligence, loading and interpreting the intelligence, and connecting the intelligence’s predictions back to the rest of the system.
Geoff Hulten

Chapter 13. Where Intelligence Lives

Abstract
When building an Intelligent System you’ll need to decide where the intelligence should live. That is, where you will bring the model, the runtime, and the context together to produce predictions—and then how you will get those predictions back to the intelligent experience.
Geoff Hulten

Chapter 14. Intelligence Management

Abstract
The intelligence in an Intelligent System takes a journey from creation, to verification, to deployment, to lighting up for users, and finally to being monitored over time. Intelligence management bridges the gap between intelligence creation (which is discussed in Part 4 of this book) and intelligence orchestration (which is discussed in Part 5), by making it safer and easier to deploy new intelligence and enable it for users.
Geoff Hulten

Chapter 15. Intelligent Telemetry

Abstract
A telemetry system is responsible for collecting observations about how users are interacting with your Intelligent System and sending some or all of these observations back to you.
Geoff Hulten

Creating Intelligence

Frontmatter

Chapter 16. Overview of Intelligence

Abstract
So you have an Internet smart-toaster and you need to decide how long it should toast; or you have a break-time application and you need to decide when to give users a break; or you have a funny web page app and you need to decide what’s funny. We call the component of a system that makes these types of decisions the “intelligence.” The previous parts of this book helped you identify when you need intelligence, how to connect it to users through intelligent experiences, how to implement it, and where it should live. This part of the book will help you create intelligence.
Geoff Hulten

Chapter 17. Representing Intelligence

Abstract
Intelligence can be represented all sorts of ways. It can be represented by programs that test lots of conditions about the context. It can be represented by hand-labeling specific contexts with correct answers and storing them in a lookup table. It can be represented by building models with machine learning. And, of course, it can be represented by a combination of these techniques.
Geoff Hulten

Chapter 18. The Intelligence Creation Process

Abstract
Intelligence creation is the act of producing the programs, lookup tables, and models that map contexts to predictions. An effective intelligence-creation process will do all of the following
Geoff Hulten

Chapter 19. Evaluating Intelligence

Abstract
Evaluation is creation, at least when it comes to building intelligence for Intelligent Systems. That’s because intelligence creation generally involves an iterative search for effective intelligence: produce a new candidate intelligence, compare it to the previous candidate, and choose the better of the two. To do this, you need to be able to look at a pair of intelligences and answer questions like these
Geoff Hulten

Chapter 20. Machine Learning Intelligence

Abstract
Machine learning is a powerful technique for producing intelligence for large, hard, open-ended, time-changing problems. It works by showing the computer lots (and lots) of examples of contexts and the desired outcomes. The computer produces models from these examples. And the models can be used to predict the outcome for future contexts.
Geoff Hulten

Chapter 21. Organizing Intelligence

Abstract
In most large-scale systems, intelligence creation is a team activity. Multiple people can work on the intelligence at the same time, building various parts of it, or investigating different problem areas. Multiple people can also work on the intelligence over time, taking over for team members who’ve left, or revisiting an intelligence that used to work but has started having problems. Some examples of ways to organize intelligence include these
Geoff Hulten

Orchestrating Intelligent Systems

Frontmatter

Chapter 22. Overview of Intelligence Orchestration

Abstract
Intelligence orchestration is a bit like car racing. A whole team of people build a car, put all the latest technology into it, and get every aerodynamic wing, ballast, gear-ratio, and intake valve set perfectly—they make an awesome machine that can do things no other machine can do.
Geoff Hulten

Chapter 23. The Intelligence Orchestration Environment

Abstract
This chapter discusses some of the common activities that contribute to success at intelligence orchestration. These are examples of the types of things that can help get the most out of an Intelligent System, and include the following
Geoff Hulten

Chapter 24. Dealing with Mistakes

Abstract
There will be mistakes. Humans make them. Artificial intelligences make them, too—and how. Mistakes can be irritating or they can be disastrous.
Geoff Hulten

Chapter 25. Adversaries and Abuse

Abstract
Whenever you create something valuable, someone is going to try to make a buck off of it. Intelligent Systems are no different. If you spend energy, money, and time attract users, someone is going to try to make money off of those users. If you build a business that is putting pressure on a competitor, someone is going to try to make it harder for you to run that business.
Geoff Hulten

Chapter 26. Approaching Your Own Intelligent System

Abstract
Thank you for reading this book. I’m glad you got this far. You should now have the foundation to execute on your own Intelligent System project, knowing
Geoff Hulten

Backmatter

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