Organizations cannot share patients’ data directly due to concerns about privacy. A lot more problems could be solved if patients’ data could be shared across organizations without impacting their privacy. YData enables the easy sharing of data both within internal teams and external organizations.
In critical domains such as this data often exists in silos, which is insufficient to train a good model. Data augmentation is a proven method to work around this problem. YData accomplishes this without any unintended side effects by synthesizing data without changing its inherent nature.
ML models are being used in the clinical domain often for diagnosis purposes. The dataset can turn out heavily imbalanced if the subject selection is not done properly. Using our Synthesizers, you can balance the dataset and make sure that your model is not trained on the wrong data.
As generative AI reshapes industries, the quality, diversity, and safety of the data used to train and evaluate models have never been more critical. Today, we’re thrilled to announce two major additions to YData's product portfolio that...
We are thrilled to announce that YData has been recognized as the most statistically accurate synthetic data generator in AIMultiple's 2025 benchmark. This independent evaluation assessed seven publicly available synthetic data generators...
Cover photo by Andrea De Santis on Unsplash
