What is synthetic data?
As machine learning frameworks such as Tensorflow and PyTorch become easier to use and pre-designed models for computer vision and natural language processing become more common and powerful, a significant challenge data scientists face is data collection and processing.
Businesses often struggle to collect large amounts of data within a specific time frame to train accurate models. Manually labeling data is expensive and time-consuming to retrieve data. Synthetic data is an innovation that can help data scientists and businesses overcome these barriers and develop reliable machine-learning models faster.
Synthetic data sets are not constructed from records of actual events but are created by a computer program. The primary purpose of synthetic datasets is to provide a generic and robust way to train machine learning models.
