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The AI Product Manager's Handbook

By Irene Bratsis
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  1. Free Chapter
    Chapter 1: Understanding the Infrastructure and Tools for Building AI Products
About 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.
Publication date:
February 2023
Publisher
Packt
Pages
250
ISBN
9781804612934

 

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...

About the Author
  • Irene Bratsis

    Irene Bratsis is a director of digital product and data at the International WELL Building Institute (IWBI). She has a bachelor's in economics, and after completing various MOOCs in data science and big data analytics, she completed a data science program with Thinkful. Before joining IWBI, Irene worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin, and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has coordinated various AI accelerators, moderated countless events with a speaker series with Women in AI called WaiTalk, and runs a monthly book club focused on data and AI books.

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