Overview of the Djinn workflow

After you set up your Djinn account, you use the following steps to create and analyze a trained model based on source data.
Overview diagram of the Djinn workflow
  1. 1.
    To get started, you create a workspace. For hosted accounts, when you create your account, you are immediately prompted to create and configure a workspace. In the workspace configuration, you identify the source of the data to use to create the model. You can connect to an existing database, or you can upload .csv files that contain the data.
  2. 2.
    Next, you create and configure the model. The model configuration starts with a SQL query to retrieve the set of data to use in the model. You then configure the model parameters to guide the model training. You can also adjust the column data types in the query results.
  3. 3.
    After you complete the model configuration, you train the model. When it trains a model, Djinn uses the model configuration to generate new, de-identified data that is based on the SQL query results.
  4. 4.
    You then analyze the resulting model data. In Djinn, the Model Synthesis Report contains visualizations that provide insight into how well the generated data replicates the shape of the original data. You can export the model to use for further analysis. The exported model allows you to generate samples of synthetic data in your Python workflow. You can export the model to a Jupyter notebook that is based on a template provided by Djinn. Or you can export a code snippet to use as a starting point for your own Jupyter notebook. From the Jupyter notebook, you can sample the generated model data to use in other analysis tools.