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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
Gretel Relational
Perfectly preserve relationships across tables
Gretel Evaluate
Validate the quality of your synthetic data
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Gretel CLI
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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
Gretel Cloud ->
Sign up free for our Gretel Cloud Console.
Resources
About Gretel
News
Contact
Careers
Videos
Podcasts
All Resources
->
Solutions
Data Types
Tabular
Free-text
Time series
Relational Databases
Images (beta)
Enterprise use-cases
Improve ML performance
Anonymize sensitive data and databases
Enable safe data collaboration and sharing
Remove bias, balance, and boost limited data sets
Developers
Tools and Resources
Gretel CLI
Install the Gretel CLI tool
Blueprints
Run example notebooks for advanced use cases
GitHub
Open source projects and tools
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Quickstart
Examples
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Environment setup
Gretel fundamentals
Documentation ->
Get started by reading our docs.
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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?