Who should use the Generate synthetic data workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
6-step pipeline — follow each step in order
1Setup · Step 1 of 6
Open task pageAutomate data labeling
Prepare inputs and settings through Automate data labeling before running generate synthetic data.
Why this matters
Automate data labeling sets up the foundation for generate synthetic data; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
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2Input · Step 2 of 6
Open task pageAnnotate training data
Use Annotate training data to build supporting assets that improve generate synthetic data quality.
Why this matters
Annotate training data strengthens generate synthetic data by feeding better supporting material into the pipeline.
What you get
Supporting assets from annotate training data are prepared and connected to the main workflow.
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3Core · Step 3 of 6
Open task pageGenerate synthetic data
Execute generate synthetic data with Generate synthetic data to produce the primary decision-ready insight.
Why this matters
This is the core step where generate synthetic data actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass decision-ready insight is generated and ready for refinement in the next steps.
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4Refine · Step 4 of 6
Open task pageSimulate edge cases and future scenarios using synthetic data.
Refine and validate generate synthetic data output using Simulate edge cases and future scenarios using synthetic data. before final delivery.
Why this matters
Simulate edge cases and future scenarios using synthetic data. adds quality control so issues are caught before the workflow is finalized.
What you get
The decision-ready insight is improved, validated, and prepared for final delivery.
5Refine · Step 5 of 6
Open task pageGenerate synthetic data that mimics real-world datasets.
Refine and validate generate synthetic data output using Generate synthetic data that mimics real-world datasets. before final delivery.
Why this matters
Generate synthetic data that mimics real-world datasets. adds quality control so issues are caught before the workflow is finalized.
What you get
The decision-ready insight is improved, validated, and prepared for final delivery.
6Deliver · Step 6 of 6
Open task pageData Masking
Package and ship the output through Data Masking so generate synthetic data reaches end users.
Why this matters
Data Masking is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized decision-ready insight is ready for publishing, handoff, or integration.
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