---
product_id: 522295954
title: "The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems"
price: "€ 71.10"
currency: EUR
in_stock: true
reviews_count: 10
url: https://www.desertcart.pt/products/522295954-the-ai-product-managers-handbook-develop-a-product-that-takes
store_origin: PT
region: Portugal
---

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

**Price:** € 71.10
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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
- **How much does it cost?** € 71.10 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.pt](https://www.desertcart.pt/products/522295954-the-ai-product-managers-handbook-develop-a-product-that-takes)

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## Description

Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and commercial success Take macro data and use it to show your customers you’re a source of truth Best practices and common pitfalls that impact companies while developing AI product Book Description 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. What you will learn Build AI products for the future using minimal resources Identify opportunities where AI can be leveraged to meet business needs Collaborate with cross-functional teams to develop and deploy AI products Analyze the benefits and costs of developing products using ML and DL Explore the role of ethics and responsibility in dealing with sensitive data Understand performance and efficacy across verticals Who this book is for This book is for product managers and other professionals interested in incorporating AI into their products. Foundational knowledge of AI is expected. If you understand the importance of AI as the rising fourth industrial revolution, this book will help you surf the tidal wave of digital transformation and change across industries. Table of Contents Understanding the Infrastructure and Tools for Building AI Products Model Development and Maintenance for AI Products Machine Learning and Deep Learning Deep Dive Commercializing AI Products AI Transformation and Its Impact on Product Management Understanding the AI-Native Product Productizing the ML Service Customization for Verticals, Customers, and Peer Groups Macro and Micro AI for Your Product Benchmarking Performance, Growth Hacking, and Cost The Rising Tide of AI Trends and Insights across Industry Evolving Products into AI Products

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 | #1,759,590 in Books ( See Top 100 in Books ) #134 in Business Research & Development #263 in Machine Theory (Books) #3,254 in Artificial Intelligence & Semantics |
| Customer Reviews | 4.2 out of 5 stars 46 Reviews |

## 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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*Last updated: 2026-06-08*