Final Up to date on July 15, 2022
When you’re an information engineer or information scientist, you understand how laborious it’s to generate and preserve real looking information at scale. And to ensure information privateness safety, along with all of your day-to-day tasks? OOF. Discuss a heavy carry.
However in right this moment’s world, environment friendly information de-identification is now not optionally available for groups that have to construct, take a look at, remedy, and analyze in fast-paced environments. The rise in ever-stronger information privateness laws make de-identification a requirement, and the growing complexity and scale of right this moment’s information make de-identifying it a monumental problem. Many groups attempt to deal with this in home…and lose hours out of their day consequently, solely to seek out that their generated information isn’t real looking sufficient for efficient use.
There’s a higher means, Djinn by Tonic.ai.
As an alternative of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic right this moment’s information whereas integrating seamlessly into your current workflows. Tonic.ai’s artificial information options allow you to create high-fidelity information that’s helpful, protected, and simple to supply—and it meets the wants of each information scientists and information engineering alike.
Djinn by Tonic.ai gives information groups:
- Practice fashions inside Djinn to hydrate ML workflows with real looking artificial information
- Work throughout databases to construct personalized views and export instantly into Jupyter notebooks
- Seize advanced relationships inside your information throughout interdependent columns and rows
- Make use of deep neural community generative fashions on the innovative of information synthesis
- Achieve confidence in your information’s privateness and in your mannequin’s suitability for ML functions
- Validate the privateness of your information with comparative reviews inside your Jupyter pocket book
- Hook up with main relational databases and information warehouses. Streamline and maximize your workflows through API
- Really feel safe figuring out that your information by no means leaves your surroundings
Make the most of your current information whether or not it’s for testing, coaching ML fashions, or unlocking information evaluation. Reply nuanced scientific questions, allow higher testing, and help enterprise selections with the artificial information that appears, feels, and behaves like your manufacturing information – as a result of it’s made out of your manufacturing information. For extra data or a demo, go to our web site. When you’d wish to give the platform a take a look at run your self, we provide that too.