Find out which of Gretel's APIs and models best fit your use cases with data.
When creating a new Gretel model, the type of model you create depends on your use case. This post will discuss a few primary use cases and help you identify which API best suits your needs.
I want to:
Create synthetic data with privacy guarantees. API: SYNTHETICS
Create models and share unlimited amounts of realistic synthetic data to shorten long lead times to acquire data and to address privacy and legal issues. Read about the Synthetics API.
Address insufficient examples of edge cases.API: SYNTHETICS
Use synthetic data to augment data sources, improve accuracy, and reduce bias in machine learning models. Read about the Synthetics API.
Anonymize data to power my testing or staging environment. API: TRANSFORMS
Create real-time transformations to anonymize and power development, test, and staging pipelines with all of the dynamism of real data. Read about the Transform API.
Detect sensitive data. API: CLASSIFY
Detect sensitive information in data tables, warehouses, and log streams. Extend 40+ managed detections by authoring your own entity detections with regular expressions, all defined from a simple YAML configuration policy. Read about Classify API.