Last Updated on July 15, 2022
If you’re a data engineer or data scientist, you know how hard it is to generate and maintain realistic data at scale. And to guarantee data privacy protection, in addition to all your day-to-day responsibilities? OOF. Talk about a heavy lift.
But in today’s world, efficient data de-identification is no longer optional for teams that need to build, test, solve, and analyze in fast-paced environments. The rise in ever-stronger data privacy regulations make de-identification a requirement, and the increasing complexity and scale of today’s data make de-identifying it a monumental challenge. Many teams try to tackle this in house…and lose hours out of their day as a result, only to find that their generated data isn’t realistic enough for effective use.
There is a better way, Djinn by Tonic.ai.
Instead of cumbersome workarounds or outdated legacy tools, get a platform built to work with and mimic today’s data while integrating seamlessly into your existing workflows. Tonic.ai’s synthetic data solutions enable you to create high-fidelity data that is useful, safe, and easy to source—and it meets the needs of both data scientists and data engineering alike.
Djinn by Tonic.ai offers data teams:
- Train models within Djinn to hydrate ML workflows with realistic synthetic data
- Work across databases to build customized views and export directly into Jupyter notebooks
- Capture complex relationships within your data across interdependent columns and rows
- Employ deep neural network generative models at the cutting edge of data synthesis
- Gain confidence in your data’s privacy and in your model’s suitability for ML applications
- Validate the privacy of your data with comparative reports within your Jupyter notebook
- Connect to leading relational databases and data warehouses. Streamline and maximize your workflows via API
- Feel secure knowing that your data never leaves your environment
Take advantage of your existing data whether it be for testing, training ML models, or unlocking data analysis. Answer nuanced scientific questions, enable better testing, and support business decisions with the synthetic data that looks, feels, and behaves like your production data – because it’s made from your production data. For more information or a demo, visit our website. If you’d like to give the platform a test run yourself, we offer that too.
Image and article originally from machinelearningmastery.com. Read the original article here.