Build customized views of only the data you need, working across your database, with no external scripting required. Train models on your views, then export those models directly into a Jupyter notebook to hydrate your ML workflows with synthetic data.
Capture complex relationships across columns of categorical, continuous, and location data, as well as the most nuanced relationships between interdependent rows of events data, thanks to Djinn’s deep neural network generative models.
Validate the privacy of your data with reports comparing your real data to your synthetic data within your Jupyter notebook. Gain confidence in the privacy you’ve achieved and in your model’s suitability for ML applications.
Enable your developers, unblock your data scientists, and respect data privacy as a human right.