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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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If you happen to’re a knowledge engineer or knowledge scientist, you know the way exhausting it’s to generate and preserve life like knowledge at scale. And to ensure knowledge privateness safety, along with all of your day-to-day tasks? OOF. Discuss a heavy raise.

However in in the present day’s world, environment friendly knowledge de-identification is not elective for groups that have to construct, check, resolve, and analyze in fast-paced environments. The rise in ever-stronger knowledge privateness laws make de-identification a requirement, and the rising complexity and scale of in the present day’s knowledge make de-identifying it a monumental problem. Many groups attempt to sort out this in home…and lose hours out of their day because of this, solely to search out that their generated knowledge isn’t life like sufficient for efficient use.

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

As a substitute of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic in the present day’s knowledge whereas integrating seamlessly into your present 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 gives knowledge groups:

Built-in Workflows

  • Prepare fashions inside Djinn to hydrate ML workflows with life like artificial knowledge
  • Work throughout databases to construct custom-made 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 knowledge 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 through API
  • Really feel safe figuring out that your knowledge by no means leaves your setting

Benefit from your present 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 help enterprise choices with the artificial knowledge that appears, feels, and behaves like your manufacturing knowledge – as a result of it’s made out of your manufacturing knowledge. For extra data or a demo, go to our web site. If you happen to’d wish to give the platform a check run your self, we provide that too.

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