---
product_id: 522295954
title: "The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems"
price: "¥13535"
currency: JPY
in_stock: true
reviews_count: 7
url: https://www.desertcart.jp/products/522295954-the-ai-product-managers-handbook-develop-a-product-that-takes
store_origin: JP
region: Japan
---

# The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems

**Price:** ¥13535
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- **What is this?** The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems
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## Description

The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems [Irene Bratsis] on desertcart.com. *FREE* shipping on qualifying offers. The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems

Review: Robust, fundamental, nuanced, and incredibly useful - As a seasoned consultant with a focus on AI and Automation, I've found 'The AI Product Manager's Handbook' by Irene Bratsis to be a critical resource in my professional toolkit. This book adeptly introduces fundamental machine learning concepts tailored specifically for AI product managers. It achieves a commendable balance, offering sufficient technical insights to empower product managers in AI discussions, while remaining accessible to those without a technical background. Bratsis emphasizes the significance of collaborating with stakeholders and acting as a bridge among various departments involved in AI projects. Additionally, she thoughtfully addresses AI ethics, particularly the need for representative datasets, moving beyond superficial treatment of ethical concerns in AI implementation within organizations. A notable strength of the book is Bratsis' structured approach to developing AI products, covering both AI-centric products and the incorporation of AI into existing products. This methodological perspective is particularly valuable, as the field of AI Product Development, although increasingly recognized, lacks a clearly defined framework. Bratsis' contribution in this regard, outlining a comprehensive process for driving AI products from conception to deployment, is commendable. The book is enriched with practical examples that vividly bring AI product concepts to life. These examples are especially beneficial for readers like myself, who are less technically inclined, offering clear insights into the practicalities and significance of AI solutions. It stands as an indispensable guide for industry leaders keen on successfully steering and launching AI initiatives. While Bratsis extensively covers AI ethics, a thought that struck me during my reading was the under-discussed topic of 'truth' in datasets. While issues of bias and representation receive deserved attention, the importance of training machine learning models on reliable and truthful data sources is less frequently addressed. This leads to critical considerations, such as whether legal models are trained on sensational media articles or actual court filings, or if scientific AI models are based on peer-reviewed research or popular science commentary. Recognizing bias as a relative term, it is crucial to prioritize truthfulness as a fundamental standard. The book, largely written pre-chatGPT, possesses an authenticity that is refreshing. Since it predates the surge of generative AI like chatGPT, it feels more grounded in the foundational aspects of AI and ML, steering clear of the latest buzzwords and trends like 'Prompt Engineering.' However, an exploration of how generative AI might reshape the role of AI Product Managers would have been a valuable addition. Nevertheless, this aspect only heightens my anticipation for future updates from Bratsis. In conclusion, 'The AI Product Manager's Handbook' is an essential read for anyone engaged in AI product management. Bratsis' insights, combined with real-world examples, offer a clear and practical guide for leading successful AI product initiatives. The book is particularly recommended for leaders seeking to adeptly manage AI integration in a variety of sectors, maintaining a balanced and ethically responsible approach.
Review: Great resource book but received a misprinted edition - Great resource for AI POs. Entire book I received has print quality issues though: entire paragraphs cut off or missing.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #1,768,580 in Books ( See Top 100 in Books ) #29 in Business Research & Development #287 in Machine Theory (Books) #808 in Artificial Intelligence & Semantics |
| Customer Reviews | 3.8 3.8 out of 5 stars (45) |
| Dimensions  | 7.5 x 0.57 x 9.25 inches |
| Edition  | 1st |
| ISBN-10  | 1804612936 |
| ISBN-13  | 978-1804612934 |
| Item Weight  | 15.3 ounces |
| Language  | English |
| Print length  | 250 pages |
| Publication date  | February 28, 2023 |
| Publisher  | Packt Publishing |

## Images

![The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems - Image 1](https://m.media-amazon.com/images/I/71I3vI2QVyL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Robust, fundamental, nuanced, and incredibly useful
*by J***G on December 4, 2023*

As a seasoned consultant with a focus on AI and Automation, I've found 'The AI Product Manager's Handbook' by Irene Bratsis to be a critical resource in my professional toolkit. This book adeptly introduces fundamental machine learning concepts tailored specifically for AI product managers. It achieves a commendable balance, offering sufficient technical insights to empower product managers in AI discussions, while remaining accessible to those without a technical background. Bratsis emphasizes the significance of collaborating with stakeholders and acting as a bridge among various departments involved in AI projects. Additionally, she thoughtfully addresses AI ethics, particularly the need for representative datasets, moving beyond superficial treatment of ethical concerns in AI implementation within organizations. A notable strength of the book is Bratsis' structured approach to developing AI products, covering both AI-centric products and the incorporation of AI into existing products. This methodological perspective is particularly valuable, as the field of AI Product Development, although increasingly recognized, lacks a clearly defined framework. Bratsis' contribution in this regard, outlining a comprehensive process for driving AI products from conception to deployment, is commendable. The book is enriched with practical examples that vividly bring AI product concepts to life. These examples are especially beneficial for readers like myself, who are less technically inclined, offering clear insights into the practicalities and significance of AI solutions. It stands as an indispensable guide for industry leaders keen on successfully steering and launching AI initiatives. While Bratsis extensively covers AI ethics, a thought that struck me during my reading was the under-discussed topic of 'truth' in datasets. While issues of bias and representation receive deserved attention, the importance of training machine learning models on reliable and truthful data sources is less frequently addressed. This leads to critical considerations, such as whether legal models are trained on sensational media articles or actual court filings, or if scientific AI models are based on peer-reviewed research or popular science commentary. Recognizing bias as a relative term, it is crucial to prioritize truthfulness as a fundamental standard. The book, largely written pre-chatGPT, possesses an authenticity that is refreshing. Since it predates the surge of generative AI like chatGPT, it feels more grounded in the foundational aspects of AI and ML, steering clear of the latest buzzwords and trends like 'Prompt Engineering.' However, an exploration of how generative AI might reshape the role of AI Product Managers would have been a valuable addition. Nevertheless, this aspect only heightens my anticipation for future updates from Bratsis. In conclusion, 'The AI Product Manager's Handbook' is an essential read for anyone engaged in AI product management. Bratsis' insights, combined with real-world examples, offer a clear and practical guide for leading successful AI product initiatives. The book is particularly recommended for leaders seeking to adeptly manage AI integration in a variety of sectors, maintaining a balanced and ethically responsible approach.

### ⭐⭐⭐⭐ Great resource book but received a misprinted edition
*by A***. on March 18, 2024*

Great resource for AI POs. Entire book I received has print quality issues though: entire paragraphs cut off or missing.

### ⭐⭐⭐⭐⭐ Bridging the AI Aspiration and Execution Gap
*by M***W on October 15, 2023*

The journey from a traditional product management realm to one intertwined with data, machine learning, and other advanced Data Science methodologies has been a roller-coaster of sorts in my career. “The AI Product Managers Handbook” by Irene Bratsis has served as a sagely companion in this voyage. It not only validated the struggles typical to AI/ML teams but also laid down a pragmatic roadmap to navigate through them. I particularly relished the balanced discourse on commencing an AI product journey and infusing AI into existing products. Bratsis’ caution against being carried away by the current AI euphoria, while reiterating the bedrock principles of modern product management, was a timely reminder. I wholeheartedly recommend this book to Product Managers either entrenched in or intrigued by the conjunction of AI and product development.

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*Product available on Desertcart Japan*
*Store origin: JP*
*Last updated: 2026-02-17*