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

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

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

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

by Irene Bratsis

eBook

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Overview

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.


Product Details

ISBN-13: 9781804617335
Publisher: Packt Publishing
Publication date: 02/28/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 250
File size: 3 MB

About the Author

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.

Table of Contents

Table of Contents
  1. Understanding the Infrastructure and Tools for Building AI Products
  2. Model Development and Maintenance for AI Products
  3. Machine Learning and Deep Learning Deep Dive
  4. Commercializing AI Products
  5. AI Transformation and Its Impact on Product Management
  6. Understanding the AI-Native Product
  7. Productizing the ML Service
  8. Customization for Verticals, Customers, and Peer Groups
  9. Macro and Micro AI for Your Product
  10. Benchmarking Performance, Growth Hacking, and Cost
  11. The Rising Tide of AI
  12. Trends and Insights across Industry
  13. Evolving Products into AI Products
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