You're reading from The AI Product Manager's Handbook Develop a product that takes advantage of machine learning to solve AI problems

Product type Paperback

Published in Feb 2023

Publisher Packt

ISBN-13 9781804612934

Length 250 pages

Edition 1st Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Irene Bratsis

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well

2. Chapter 1: Understanding the Infrastructure and Tools for Building AI Products FREE CHAPTER

3. Chapter 2: Model Development and Maintenance for AI Products

4. Chapter 3: Machine Learning and Deep Learning Deep Dive

5. Chapter 4: Commercializing AI Products

6. Chapter 5: AI Transformation and Its Impact on Product Management

7. Part 2 – Building an AI-Native Product

8. Chapter 6: Understanding the AI-Native Product

9. Chapter 7: Productizing the ML Service

10. Chapter 8: Customization for Verticals, Customers, and Peer Groups

11. Chapter 9: Macro and Micro AI for Your Product

12. Chapter 10: Benchmarking Performance, Growth Hacking, and Cost

13. Part 3 – Integrating AI into Existing Non-AI Products

14. Chapter 11: The Rising Tide of AI

15. Chapter 12: Trends and Insights across Industry

16. Chapter 13: Evolving Products into AI Products

17. Index

Why subscribe?

18. Other Books You May Enjoy

Testing and troubleshooting

In Chapter 1, we discussed the idea of continuous maintenance, which included continuous integration, continuous delivery, continuous training, and continuous monitoring. This section will build on that and expand on how to test and troubleshoot issues related to ML products on an ongoing basis so that your product is set up for success. Once you’ve made your first deployment, we jump right into the continuous training and continuous maintenance portion of the continuous maintenance process we discussed in Chapter 1.

Remember, managing the performance of your models post-deployment is crucial and it will be a highly iterative, never-ending process of model maintenance. As is the case with traditional software development, you will continue to test, troubleshoot, and fix bugs for your AI/ML products as well. The only difference is that you will also screen for lags in performance and bugs related to your model.

Continuously monitoring your model...