An AI product manager needs to have a comprehensive understanding of AI, along with all the varied components that lead to its success, if they’re going to be successful in commercializing their products. This first part consists of five chapters that will cover what the term AI encompasses and how to support infrastructure to make it successful within your organization. It will also cover how to support your AI program from a maintenance perspective, navigate the vast areas of machine learning (ML) and deep learning (DL), choose the best path for your product, and understand current and future developments in AI products.
By the end of this part, you will understand AI terms and components, what AI implementation means from an investment perspective, how to maintain AI products sustainably, and how to choose between the types of AI that would best fit your product and market. You will also learn about the success factors for ideating and building a minimum viable product (MVP) and how to make a product that truly serves its market.
This part comprises the following chapters:
- Chapter 1, Understanding the Infrastructure and Tools for Building AI Products
- Chapter 2, Model Development and Maintenance for AI Products
- Chapter 3, Deep Learning Deep Dive
- Chapter 4, Commercializing AI Products
- Chapter 5, AI Transformation and Its Impact on Product Management