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.
Synthetic data, artificially generated data that mimics real-world data, is a technology that has undergone significant transformation in recent years. Since the dawn of data-driven synthetic data generation with generative models,...
Photo by Roman Kraft on Unsplash As machine learning continues to evolve, the use of Large Language Models (LLMs) has become increasingly prevalent, particularly in complex tasks requiring deep understanding and generation of human-like...
Photo by Isaac Smith on Unsplash Synthetic data bootstrap In the dynamic landscape of organizations high-quality data is a requirement for the development of many solutions - from software testing and validation all the way to Artificial...
