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

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Final Up to date on July 15, 2022

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When you’re an information engineer or knowledge scientist, you know the way onerous it’s to generate and keep practical knowledge at scale. And to ensure knowledge privateness safety, along with all of your day-to-day obligations? OOF. Speak about a heavy raise.

However in at this time’s world, environment friendly knowledge de-identification is not non-compulsory for groups that have to construct, take a look at, clear up, and analyze in fast-paced environments. The rise in ever-stronger knowledge privateness rules make de-identification a requirement, and the growing complexity and scale of at this time’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 practical sufficient for efficient use.

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

As a substitute of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic at this time’s knowledge whereas integrating seamlessly into your current workflows.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 simple to supply—and it meets the wants of each knowledge scientists and knowledge engineering alike.

Djinn by Tonic.ai provides knowledge groups:

Built-in Workflows

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

Knowledge 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

Knowledge Privateness

  • Acquire confidence in your knowledge’s privateness and in your mannequin’s suitability for ML functions
  • Validate the privateness of your knowledge with comparative studies inside your Jupyter pocket book

Platform Options

  • Hook up 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 setting

Benefit from 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 choices with the artificial knowledge that appears, feels, and behaves like your manufacturing knowledge – as a result of it’s constituted of your manufacturing knowledge. For extra data or a demo, go to our web site. When you’d prefer to give the platform a take a look at run your self, we provide that too.

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