Book Image

The AI Product Manager's Handbook

By : Irene Bratsis
4 (2)
Book Image

The AI Product Manager's Handbook

4 (2)
By: Irene Bratsis

Overview of this book

Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You’ll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You’ll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you’ll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you’ll have understood how to navigate the world of AI from a product perspective.
Table of Contents (19 chapters)
1
Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
7
Part 2 – Building an AI-Native Product
13
Part 3 – Integrating AI into Existing Non-AI Products

Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well

An AI product manager needs to have a comprehensive understanding of AI, along with all the varied components that lead to its success, if they’re going to be successful in commercializing their products.

This first part consists of five cumulative chapters that will cover what the term AI encompasses and how to support infrastructure to make it successful within your organization. It will also cover how to support your AI program from a maintenance perspective, how to navigate the vast areas of machine learning (ML) and deep learning (DL) and choose the best path for your product, and how to understand current and future developments in AI products.

By the end of this part, you will understand AI terms and components, what an AI implementation means from an investment perspective, how to maintain AI products sustainably, and how to choose between the types of AI that would...