Final Up to date on July 15, 2022
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For those who’re an information engineer or knowledge scientist, you know the way arduous it’s to generate and keep reasonable 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 right this moment’s world, environment friendly knowledge de-identification is now not non-obligatory for groups that have to construct, take a look at, resolve, and analyze in fast-paced environments. The rise in ever-stronger knowledge privateness rules make de-identification a requirement, and the rising complexity and scale of right this moment’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 because of this, solely to seek out that their generated knowledge isn’t reasonable 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As a substitute of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic right this moment’s knowledge whereas integrating seamlessly into your current workflows.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 presents knowledge groups:
Built-in Workflows
- Practice fashions inside Djinn to hydrate ML workflows with reasonable artificial knowledge
- Work throughout databases to construct personalized views and export straight into Jupyter notebooks
Knowledge Constancy
- Seize complicated relationships inside your knowledge throughout interdependent columns and rows
- Make use of deep neural community generative fashions on the leading edge of knowledge 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 reviews 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 realizing 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 help 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. For those who’d prefer to give the platform a take a look at run your self, we provide that too.
