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Tonic User Guide
About Tonic
Getting started with Tonic
Managing your Tonic account
Frequently Asked Questions
Creating and managing workspaces
Managing workspaces
Managing access to workspaces
Viewing workspace jobs and job details
Configuring data generation
Using Privacy Hub to identify and protect sensitive data
Database View
Table View
Table modes
Generators
Subsetting data
Viewing and adding foreign keys
Viewing and resolving schema changes
Tracking changes to the data generation configuration
Using the Privacy Report to verify data protection
Running data generation
Running a data generation job
Managing Tonic data generation performance
Post-job scripts
Webhooks
Configuring data science modeling
Data science modeling prerequisites
Viewing the list of models
Creating a model configuration
Configuring a model
Setting and running the model SQL query
Configuring the model parameters
Adjusting the column types
Viewing, editing, and deleting a model
Training and exporting data models
Training a model
Reviewing the training results
Exporting a model
Installing and Administering Tonic
Tonic architecture
Using Tonic securely
Deploying a self-hosted Tonic instance
Managing user access to Tonic
Tonic monitoring and logging
Setting environment variables
Updating Tonic
Connecting to your data
About data connectors
Data connector summary
Amazon EMR
Amazon Redshift
Databricks
Google BigQuery
MongoDB
MySQL
Oracle
PostgreSQL
Snowflake on AWS
Snowflake on Azure
Spark SDK
Spark with Livy
SQL Server
Using the Tonic API
About the Tonic API
Getting an API token
Getting the workspace ID
Using the Tonic API to perform tasks
Example script: Starting a data generation job
Example script: Polling for a job status and creating a Docker package
Other resources
Release notes
Tonic tutorial videos
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GitBook
Configuring a model
The data model configuration includes the following elements.
Run a SQL query
The query results provide the underlying data for the model.
Configure the model parameters
The model parameters guide the model training.
Adjust the column types
Update the column types for the model data as needed.
Configuring data science modeling - Previous
Creating a model configuration
Next
Setting and running the model SQL query
Last modified
25d ago
