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You're reading from  The AI Product Manager's Handbook

Product typeBook
Published inFeb 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781804612934
Edition1st Edition
Languages
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Author (1)
Irene Bratsis
Irene Bratsis
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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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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, gives 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, Macro and Micro AI for Your Product, gives an overview of the various ways you can leverage AI in ways big and small, as well as some of the most successful examples and common mistakes.

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, The Rising Tide of AI, is a revisit to the concept of the fourth industrial revolution and a blueprint for products that don’t currently leverage AI.

Chapter 12, Trends and Insights across Industry, dives into the various ways we’re seeing AI trending across industries, based on prominent and respected research organizations.

Chapter 13, 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.

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The AI Product Manager's Handbook
Published in: Feb 2023Publisher: PacktISBN-13: 9781804612934

Author (1)

author image
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.
Read more about Irene Bratsis