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Forbes: Synthetic Data Is About To Transform AI
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Gretel Toolkit
Synthetics
Generate unlimited synthesized datasets.
Transform
Perform privacy-preserving transformations on sensitive data.
Classify
Identify PII with advanced NLP detection.
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Gretel's Toolkit
Gretel’s toolkit includes Synthetics, Transform and Classify APIs to provide you with a complete set of tools to create safe data.
Synthetics
Generate unlimited synthesized datasets.
Transform
Perform privacy-preserving transformations on sensitive data.
Classify
Identify PII with advanced NLP detection.
Developers
Documentation
Get started creating safe data by reading our docs.
Gretel CLI
Install the gretel-client CLI tool.
REST API reference
An open source data transformation library and bindings to Gretel APIs.
Get help on Slack
Join our Slack community and ask questions to Gretel team members.
GitHub
View our open source projects and SDKs on GitHub.
Getting started
Environment Setup
Architecture and Components
Configure your model
Tutorials
Create Synthetic Data
Balance a Dataset
Redact Sensitive Data
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From the blog
Test Data Generation: Uses, Benefits, and Tips
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Create Synthetic Time-series Data with DoppelGANger and PyTorch
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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?