Synthetic Data for Machine Learning

By neub9
3 Min Read

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:

  1. The Value of Synthetic Data
  2. Synthetic Data for Edge Cases
  3. How to Generate Synthetic Data

Problems with Real Data and the Uniqueness of Synthetic Data

Privacy issues in healthcare data, safety concerns, scalability issues with real data collection, and the difficulty of manual labeling of real data can be mitigated with synthetic data. An example of this is the creation of privacy-preserving synthetic electronic health records at Google. Synthetic data can also address the problem of dangerous real data collection, as well as the scalability and manual labeling challenges in different fields like healthcare and self-driving applications.

Generating Synthetic Data for Edge Cases

A major strength of synthetic data is that more can always be generated. It also comes with the benefit of already being labeled. There are many ways to generate synthetic data and which one you choose depends on your use case. These methods include statistical methods, data augmentation/CGI, and generative AI, each with their own strengths and limitations.

Discussing Synthetic Data Creation Methods

  • Statistical Methods
  • Data Augmentation/CGI
  • Generative AI

If a project doesn’t have enough high-quality and diverse real data, synthetic data might be an option. If you have any questions or thoughts on this blog post, feel free to reach out in the comments below or through Twitter.

Michael Galarnyk is a Data Science Professional, and works in Product Marketing Content Lead at Parallel Domain.

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