Synthetic Data for Machine Learning

Author(s): Michael Galarnyk

Originally published on Towards AI.

It’s no secret that supervised machine learning models need to be trained on high-quality labeled datasets. However, collecting enough high-quality labeled data can be a significant challenge, especially in situations where privacy and data availability are major concerns. Fortunately, this problem can be mitigated with synthetic data. Synthetic data is data that is artificially generated rather than collected from real-world events. This data can either augment real data or can be used in place of real data. It can be created in several ways, including through the use of statistics, data augmentation/computer-generated imagery (CGI), or generative AI, depending on the use case. In this post, we will go over:

The Value of Synthetic DataSynthetic Data for Edge CasesHow to Generate Synthetic Data

Problems with real data have led to many use cases for synthetic data, which you can check out below.

Image by Google Research

Healthcare data is widely known to have privacy restrictions. For example, while incorporating electronic health records (EHR) into machine learning applications could enhance patient outcomes, doing so while adhering to patient privacy regulations like HIPAA is difficult. Even techniques to anonymize data aren’t perfect. In response, researchers at Google came up with EHR-Safe, which is a framework for generating… Read the full blog for free on Medium.

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Published via Towards AI