Another way is to allow users to provide feedback: make it easy for users to provide feedback anytime, anywhere. Let users help you find unknown unknowns, or other types of errors. You can also use user feedback to improve your system. E.g. executive email list YouTube allows users to tell the system certain suggestions they don't want to see. It also uses this to collect more data to make its recommendations more personal and accurate. Making ML model executive email list predictions as suggestions, without forcing users to do so, is also a way to manage user expectations. You can give the user multiple options without specifying what the user should do.
Note, however, that this method won't work if the user doesn't have enough information to make the right decision. Many of the general principles we talked about executive email list earlier still apply here. You can find more details in my last article. How to design and manage AI products? Define the problem and test it early: If you hear someone say "Let's build an ML model executive email list first and see what it can do." Be careful, trying to develop a product without defining the problem often wastes a lot of team time. Know when you should or shouldn't use ML. Plan your data strategy from day one. Building
ML products is cross-cutting and involves more than just machine learning Author: Bastiane Huang, with nearly 10 years of product and market development management executive email list experience, currently works as a product manager for an AI/Robotics startup in San Francisco, focusing on developing machine learning software for robot vision and control. This article was originally published by Bastiane on Everyone is a product manager, reprinting is prohibited without permission The executive email list title image is from Unsplash and is based on the CC0 protocol. Reward the author and encourage TA to hurry up and create! appreciate For more exciting content, please pay attention to everyone is a product manager