Who should use the Automate data labeling workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
4-step pipeline — follow each step in order
1Setup · Step 1 of 4
Open task pageGenerate synthetic data
Prepare inputs and settings through Generate synthetic data before running automate data labeling.
Why this matters
Generate synthetic data sets up the foundation for automate data labeling; 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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2Core · Step 2 of 4
Open task pageAutomate data labeling
Execute automate data labeling with Automate data labeling to produce the primary decision-ready insight.
Why this matters
This is the core step where automate data labeling 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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3Refine · Step 3 of 4
Open task pageAnnotate training data
Refine and validate automate data labeling output using Annotate training data before final delivery.
Why this matters
Annotate training 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.
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4Deliver · Step 4 of 4
Open task pageAutomate code review
Package and ship the output through Automate code review so automate data labeling reaches end users.
Why this matters
Automate code review 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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