Final Up to date on July 15, 2022
Sponsored Publish
In the event you’re an information engineer or information scientist, you know the way onerous it’s to generate and keep lifelike information at scale. And to ensure information privateness safety, along with all of your day-to-day duties? OOF. Discuss a heavy elevate.
However in right now’s world, environment friendly information de-identification is now not optionally available for groups that have to construct, check, remedy, and analyze in fast-paced environments. The rise in ever-stronger information privateness rules make de-identification a requirement, and the growing complexity and scale of right now’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 in consequence, solely to search out that their generated information isn’t lifelike sufficient for efficient use.
There’s a higher manner, Djinn by Tonic.ai.
As a substitute of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic right now’s information whereas integrating seamlessly into your present workflows. Tonic.ai’s artificial information options allow you to create high-fidelity information that’s helpful, secure, and simple to supply—and it meets the wants of each information scientists and information engineering alike.
Djinn by Tonic.ai presents information groups:
Built-in Workflows
- Practice fashions inside Djinn to hydrate ML workflows with lifelike artificial information
- Work throughout databases to construct personalized views and export straight into Jupyter notebooks
Knowledge Constancy
- 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
Knowledge Privateness
- Achieve confidence in your information’s privateness and in your mannequin’s suitability for ML purposes
- Validate the privateness of your information with comparative studies inside your Jupyter pocket book
Platform Options
- Connect with main relational databases and information warehouses. Streamline and maximize your workflows through API
- Really feel safe realizing that your information by no means leaves your setting
Reap the benefits of your present information whether or not 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 info or a demo, go to our web site. In the event you’d wish to give the platform a check run your self, we provide that too.
