Lessons Learned While Developing Machine Learning Products

So think about how to build the whole product with the following in mind: Is the data that's coming out of this product going to be good training data? That, I think, is something that should always be on the table. Some examples of how to do it right would be giving users opportunities to correct errors when they occur, and making sure that it's done in the flow of the product. It should be presented in a way that the user feels like it's going to provide value, because they are helping to fix the product and helping to improve it for their own benefit.