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Excessive-Constancy Artificial Information for Information Engineers and Information Scientists Alike


Final Up to date on July 15, 2022

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In the event you’re a knowledge engineer or knowledge scientist, you know the way arduous it’s to generate and keep lifelike knowledge at scale. And to ensure knowledge privateness safety, along with all of your day-to-day obligations? OOF. Speak about a heavy carry.

However in in the present day’s world, environment friendly knowledge de-identification is now not optionally available for groups that have to construct, take a look at, resolve, and analyze in fast-paced environments. The rise in ever-stronger knowledge privateness laws make de-identification a requirement, and the growing complexity and scale of in the present day’s knowledge 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 seek out that their generated knowledge isn’t lifelike sufficient for efficient use.

There’s a higher approach, Djinn by Tonic.ai.

As an alternative of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic in the present day’s knowledge whereas integrating seamlessly into your current workflows. Tonic.ai’s artificial knowledge options allow you to create high-fidelity knowledge that’s helpful, secure, and straightforward to supply—and it meets the wants of each knowledge scientists and knowledge engineering alike.

Djinn by Tonic.ai presents knowledge groups:

Built-in Workflows

  • Prepare fashions inside Djinn to hydrate ML workflows with lifelike artificial knowledge
  • Work throughout databases to construct personalized views and export instantly into Jupyter notebooks

Information Constancy

  • Seize advanced relationships inside your knowledge throughout interdependent columns and rows
  • Make use of deep neural community generative fashions on the innovative of information synthesis

Information Privateness

  • Achieve confidence in your knowledge’s privateness and in your mannequin’s suitability for ML purposes
  • Validate the privateness of your knowledge with comparative stories inside your Jupyter pocket book

Platform Options

  • Connect with main relational databases and knowledge warehouses. Streamline and maximize your workflows by way of API
  • Really feel safe understanding that your knowledge by no means leaves your atmosphere

Make the most of your current knowledge whether or not or not it’s for testing, coaching ML fashions, or unlocking knowledge evaluation. Reply nuanced scientific questions, allow higher testing, and assist enterprise selections with the artificial knowledge that appears, feels, and behaves like your manufacturing knowledge – as a result of it’s comprised of your manufacturing knowledge. For extra data or a demo, go to our web site. In the event you’d prefer to give the platform a take a look at run your self, we provide that too.

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