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

Irene really gives full A-Z coverage of what it looks like to lead the development of an AI product. She gives the necessary introduction to all the current relevant technologies, detail into the business fit, and a nuanced view of AI’s societal impact. This is a thorough, but approachable read that I’d recommend to anyone interested in the AI space - technical concepts are explained in such an approachable way I ordered another copy for my brother who is a college student interested in data science and this has been a really approachable introduction to ML concepts.

This book couldn't be more relevant as the AI landscape is unfolding and expanding quicker than ever! This three-part book is a great starting point for understanding the necessary tools, methods, models, and even commercialization techniques for building and managing AI and ML products. The author also discusses how to evolve existing products into AI products, as well as current trends and insights across industries.It's thorough and well-written, and provides a great overview for anyone interested in creating AI products, even those without an overly-technical background. Highly recommend!

This book caters to individuals who aspire to become AI product managers, AI technologists, and entrepreneurs, as well as those with a casual interest in the process of creating AI products. It is suitable for those already engaged in product management with a curiosity for developing AI products, and for those working in AI development who wish to integrate their knowledge into product management and transition to a more business-centric role. Although some chapters in the book have a technical focus, the technical content is designed to be beginner-friendly and accessible to all readers.

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.

As a pretty new product manager and as part of a team that is really focused on AI, I was keen to pick this up and get some deeper insights into working with AI as a product manager. This book stands out for its practical approach, demystifying complex machine learning concepts and offering actionable insights that bridge the gap between technical teams and product managers.One of the book's strongest points is its structured roadmap, which walks readers through the entire AI product lifecycle—from ideation and feasibility analysis to development and post-launch iterations. It effectively highlights the importance of collaboration between data scientists, engineers, and product managers, providing tools and frameworks to facilitate seamless communication and project alignment.Moreover, the case studies included are particularly enlightening, showcasing real-world applications of AI and the challenges encountered. These examples not only illustrate successful implementations but also provide valuable lessons from less successful attempts, making it a well-rounded educational resource.While the book is rich in detail, it remains accessible to those who may not have a deep technical background. The author's ability to explain machine learning principles in a clear and concise manner ensures that readers of varying expertise can grasp and apply the concepts.