Console
Getting to know the Gretel Console
Last updated
Getting to know the Gretel Console
Last updated
The Gretel Console provides a fast and easy way to generate synthetic data, classify and redact PII and use our AI models without having to download or install any tools. Sign up for a free Developer account and choose a use case in the dashboard to get started.
Use cases allow you to train and run any of our models in four steps. Just launch one of the cards and upload your training dataset, or use the sample dataset we provide. A configuration file is already selected for you so you don't have to tweak any parameters. Our auto-parameters and auto-privacy settings will tune the configuration to your training dataset, ensuring the highest chance of success.
While the model is running, you can track progress in the log window, train a new model, or try another use case.
When your model has completed training, you'll see your SQS (Synthetic Quality Score) and be able to download the full report along with your synthetic data from the Downloads page. We automatically generate some records for you as part of model training, and you can easily generate more using the Generate button in the Model Header.
Projects can contain one or more models of different types such as Synthetic, Classify or Transform. Think of them as folders for your models, data sources and generated data. Projects are private by default, but you can share them with your co-workers and collaborators.
Projects can be created, filtered and sorted from the Projects page. Select a project to create a new model in that project.
You can also manage your Account in the Console, view Documentation and Announcements, and create a support ticket if you need additional help.
The Members section inside each project allows you to quickly share that project with collaborators. The following access permissions are supported: Read-only, Read/Write, Administrator, Co-Owner.
Here's a quick walkthrough of creating synthetic data in the Gretel Console.
Once the model has been trained, you can use the SQS and Privacy Levels to determine whether the data meets your quality standards. If so, quickly generate more data whenever you want, or fine tune the configuration settings to improve your scores. See our tips on improving synthetic data quality.
While use case flows make it easy to train new models, you can also create one from scratch. Start by creating a new project. Click the Projects button in the sidebar, or the button in the top navigation bar.
