Final Up to date on July 15, 2022
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When you’re a knowledge engineer or knowledge scientist, you know the way laborious it’s to generate and preserve practical knowledge at scale. And to ensure knowledge privateness safety, along with all of your day-to-day duties? OOF. Discuss a heavy carry.
However in in the present day’s world, environment friendly knowledge de-identification is now not optionally available for groups that must construct, check, clear up, 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 search out that their generated knowledge isn’t practical 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As a substitute of cumbersome workarounds or outdated legacy instruments, get a platform constructed to work with and mimic in the present day’s knowledge whereas integrating seamlessly into your current workflows. Tonic.ai’s artificial knowledge options allow you to create high-fidelity knowledge that’s helpful, protected, and straightforward to supply—and it meets the wants of each knowledge scientists and knowledge engineering alike.
Djinn by Tonic.ai affords knowledge groups:
Built-in Workflows
- Practice fashions inside Djinn to hydrate ML workflows with practical artificial knowledge
- Work throughout databases to construct custom-made views and export straight 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 leading edge of information synthesis
Knowledge Privateness
- Acquire confidence in your knowledge’s privateness and in your mannequin’s suitability for ML purposes
- Validate the privateness of your knowledge with comparative reviews 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 setting
Benefit from your current knowledge whether 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 constructed from your manufacturing knowledge. For extra data or a demo, go to our web site. When you’d prefer to give the platform a check run your self, we provide that too.
