Top Synthetic Data Tools/startups For Machine Learning Models In 2023

Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets. The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance. Synthetic data is essential for dealing with privacy concerns and decreasing prejudice, and can be superior to real-world data since they are automatically tagged and can purposefully include uncommon but critical corner situations.