Building Intelligent Systems: A Guide to Machine Learning Engineering
Apress Building Intelligent Systems：A Guide to Machine Learning Engineering.pdf
Book Description
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 success
Design an intelligent user experience: Achieve your objectives and produce data to help make the Intelligent System better over time
Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice
Create intelligence: Use different approaches, including machine learning
Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want
This Book Is For
Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems.
Table of Contents
Part I: Approaching an Intelligent Systems Project
Chapter 1: Introducing Intelligent Systems
Chapter 2: Knowing When to Use Intelligent Systems
Chapter 3: A Brief Refresher on Working with Data
Chapter 4: Defining the Intelligent System’s Goals
Part II: Intelligent Experiences
Chapter 5: The Components of Intelligent Experiences
Chapter 6: Why Creating Intelligent Experiences Is Hard
Chapter 7: Balancing Intelligent Experiences
Chapter 8: Modes of Intelligent Interaction
Chapter 9: Getting Data from Experience
Chapter 10: Verifying Intelligent Experiences
Part III: Implementing Intelligence
Chapter 11: The Components of an Intelligence Implementation
Chapter 12: The Intelligence Runtime
Chapter 13: Where Intelligence Lives
Chapter 14: Intelligence Management
Chapter 15: Intelligent Telemetry
Part IV: Creating Intelligence
Chapter 16: Overview of Intelligence
Chapter 17: Representing Intelligence
Chapter 18: The Intelligence Creation Process
Chapter 19: Evaluating Intelligence
Chapter 20: Machine Learning Intelligence
Chapter 21: Organizing Intelligence
Part V: Orchestrating Intelligent Systems
Chapter 22: Overview of Intelligence Orchestration
Chapter 23: The Intelligence Orchestration Environment
Chapter 24: Dealing with Mistakes
Chapter 25: Adversaries and Abuse
Chapter 26: Approaching Your Own Intelligent System
