The Advantages and Disadvantages of Synthetic Training Data

The most obvious advantage of using synthetic training data is that it can supplement datasets that would otherwise lack sufficient examples to train a model. As a general rule, more and higher-quality training data equals better performance, so synthetic data can play a hugely important role for machine learning engineers working in fields that suffer from a scarcity of data. However, using synthetic data comes with pros and cons. Let's look at some advantages and disadvantages of using synthetic training data. When high stakes models, such as those used to run autonomous vehicles or diagnose patients, run in the real world, they need to be able to deal with edge cases.