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Gretel Synthetics
Share, create, and augment data with generative AI
Transform
Perform privacy-preserving transformations on sensitive data
Classify
Identify PII with advanced NLP detection
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Perfectly preserve relationships across tables
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Validate the quality of your synthetic data
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Improve ML robustness
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Product
Access and anonymize
Transform
Perform privacy-preserving transformations on sensitive data
Classify
Identify PII with advanced NLP detection
Generate and balance
Gretel Synthetics
Share, create, and augment data with generative AI
Gretel Relational
Perfectly preserve relationships across tables
Measure and optimize
Gretel Evaluate
Validate the quality of your synthetic data
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Request early access.
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Use cases
Safe data sharing
Improve ML robustness
Power generative AI
Industries
Finance
Healthcare
Public Sector
Resources
About Gretel
News
Contact
Careers
Videos
Podcasts
Events & Webinars
All Resources
->
Developers
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Gretel CLI
Install the Gretel CLI tool
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Run example notebooks for advanced use cases
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FAQs
Gretel-synthetics
Gretel Synthetics FAQs
What is synthetic data?
How does Gretel Synthetics create artificial data?
Is there an architecture diagram?
What kinds of data can I send to Gretel Synthetics?
What are the outputs from Gretel Synthetics?
Can I run Gretel Synthetics on premises?
What are gretel-synthetics premium features?
Do I still need to de-identify sensitive data when using gretel-synthetics?
What kinds of privacy protections can Gretel Synthetics help with?
How is Gretel-synthetics differential privacy different from traditional implementations?
How is synthetic data different from the original source data it was trained on?
How many lines of input data do I need to train a synthetic model?
How many columns of training data can I have?
How many epochs should I train my model with?
Does training a synthetic model require a GPU?
What is differential privacy?
How does Gretel-synthetics leverage differential privacy?
How does Gretel-synthetics implement differential privacy?
If my model trained in batches using differential privacy, what is my final epsilon (privacy guarantee)?
What are good epsilon (ε) and delta (δ) values in differential privacy?
How is Stochastic Gradient Descent (SGD) modified to be differentially private?
What does RDP order mean?