Here are the core fundamentals you will be familiar with after going through the next few sections:
Architecture. Review a summary of Gretel's core system components.
Deployment options. Gretel Cloud empowers you to train models and generate synthetic data without needing to manage complex operating systems or GPU configurations. Gretel Hybrid enables you to deploy the Gretel Data Plane into your own cloud tenant, providing all of Gretel's incredible features and benefits without the need for data to leave the boundaries of your own enterprise network.
Projects. Gretel Projects can be thought of as repositories that hold models. Projects are created by single users and can be shared with various permissions.
Inputs and Outputs. Gretel Models support a number of input and output data formats. For concepts related to input and output data sources like relational databases or object stores, see the Workflows and connectors section.
Creating models. Create models and train them against your source data sets.
Running models. Running models will let you generate unlimited amounts of synthetic data.
Model types. This overview page will give you a glimpse into the different possibilities when creating and training models with Gretel.
Workflows and connectors. Workflows and connectors provide an easy way to connect to sources and sinks for working with synthetic data generation at scale.