If you deal with machine learning-based systems, you know all about training data. Data must be formatted correctly and be accurate before it’s loaded into an AI model for the purpose of training the model.
Say you’re creating a fraud detection engine using a popular machine learning system in a public cloud. First you create the data set used to train the model: in this case, millions of transactional records with the fraudulent transactions labeled. This allows the model to learn what’s likely fraudulent and what isn’t. Of course, there are different types of training data, some labeled, some not.
Once trained, the model may indeed continue training by learning what’s likely fraudulent and not through experience learning. Indeed, if you had the time, the model could train itself over time by monitoring transactions that humans or other systems mark fraudulent.