What this book covers
Chapter 1, Understanding the Infrastructure and Tools for Building AI Products, offers an overview of the main concepts and areas of infrastructure for managing AI products.
Chapter 2, Model Development and Maintenance for AI Products, delves into the nuances of model development and maintenance.
Chapter 3, Machine Learning and Deep Learning Deep Dive, is a broader discussion of the difference between traditional deep learning and deep learning algorithms and their use cases.
Chapter 4, Commercializing AI Products, discusses the major areas of AI products we see in the market, as well as examples of the ethics and success factors that contribute to commercialization.
Chapter 5, AI Transformation and Its Impact on Product Management, explores the ways AI can be incorporated into the major market sectors in the future.
Chapter 6, Understanding the AI-Native Product, provides an overview of the strategies, processes, and team building needed to empower the success of an AI-native product.
Chapter 7, Productizing the ML Service, is an exploration of the trials and tribulations that may come up when building an AI product from scratch.
Chapter 8, Customization for Verticals, Customers, and Peer Groups, is a discussion on how AI products change and evolve over various types of verticals, customer types, and peer groups.
Chapter 9, Product Design for the AI-Native Product, is an overview of product design principles and concepts that are customized for products built natively with AI/ML components.
Chapter 10, Benchmarking Performance, Growth Hacking, and Cost, explains the benchmarking needed to gauge product success at the product level rather than the model performance level.
Chapter 11, Managing the AI-Native Product, reviews ongoing AI PM considerations that relate to leadership and visionary, stakeholder and operational alignment of products built natively with AI.
Chapter 12, The Rising Tide of AI, revisits the concept of the Fourth Industrial Revolution and a blueprint for products that don’t currently leverage AI.
Chapter 13, Trends and Insights across Industry, dives into the various ways we’re seeing AI trending across industries, as well as accessible routes product teams can take when enabling AI
Chapter 14, Evolving Products into AI Products, is a practical guide on how to deliver AI features and upgrade the existing logic of products to successfully update products for AI commercial success.
Chapter 15, The Role of AI Product Design, refocuses AI design and communication foundations applied to product teams that are looking to evolve traditional software products with AI/ML capabilities.
Chapter 16, Managing the Evolving AI Product, reviews ongoing AI PM considerations that relate to leadership and visionary, stakeholder and operational alignment of traditional software products adopting AI features and capabilities.
Chapter 17, Starting a Career as an AI PM, brings readers striving for AI PM careers on a journey through the theoretical and applied foundations to set up their budding careers up for success.
Chapter 18, What Does It Mean to Be a Good AI PM?, breaks down the various facets of an AI PM and the technical, business, communication, leadership and problem solving considerations for those looking to excel in the role.
Chapter 19, Maturing and Growing as an AI PM, explores the various ways AI PMs can mature in their careers through projecting their ideal AI PM roadmap, staying informed with learning paths, networking to deepen connections and sharing their experiences and wisdom with others.
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781835882849.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “One of our hyperparameters for this random forest example was setting our cross-validation to 10.
Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “ This phenomenon is called overfitting and it’s a big topic of conversation in data science and ML circles.”
Warnings or important notes appear like this.
Tips and tricks appear like this.
