How to generate synthetic data?

Forget cumbersome manual data generation and painfully long, risky data anonymization. Generate flexibly sized, realistic synthetic data with the push of a button. Simply log in with your email address, upload your table to MOSTLY AI's secure cloud environment, configure the synthesization process and you are good to go! Your machine learning models and your testers will thank you for using better-than-real synthetic datasets. For more advanced synthetic data generation features, such as synthesizing entire databases, on-premise deployment or data connectors, contact us to get access to the enterprise version! 

How safe is my data?

We are using a secure AWS cloud environment. We do not see or retain any of your uploaded data. Once the job is completed, the original dataset will be deleted. The generated synthetic data comes with an automatically generated privacy and accuracy QA report. If the synthetic data passes the privacy and accuracy checks, you can safely use it without privacy concerns. 

What is synthetic data? And why do we need it? 

Synthetic data generators with powerful AI engines can learn the patterns and correlations of a dataset. Once the AI engine was trained on a dataset, the synthetic data generator can create as much or as little statistically identical data as you need. The resulting synthetic data contains none of the original datapoints and is perfectly privacy-safe. However, synthetic data does contain all of the insights from the original data. As a result, it is perfect for AI training, software testing and to safely share with others for collaboration. The synthesization process can also be used to augment the original data, for example to remove historic bias or to upsample rare events. Synthetic data is a powerful privacy enhancing technology and a prolific data science tool. 

How can I get access to the paid version?

If you would like to use the proprietary version of MOSTLY AI's synthetic data platform with advanced data connection capabilities for synthesizing complex data structures with referential integrity, please contact us!