Who should use the Model Versioning workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
7-step pipeline — follow each step in order
1Setup · Step 1 of 7
Open task pageManaging model versions and artifacts
Prepare inputs and settings through Managing model versions and artifacts before running model versioning.
Why this matters
Managing model versions and artifacts sets up the foundation for model versioning; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Input · Step 2 of 7
Open task pageDeploy AI models
Use Deploy AI models to build supporting assets that improve model versioning quality.
Why this matters
Deploy AI models strengthens model versioning by feeding better supporting material into the pipeline.
What you get
Supporting assets from deploy ai models are prepared and connected to the main workflow.
3Input · Step 3 of 7
Open task pageTrain machine learning models
Use Train machine learning models to build supporting assets that improve model versioning quality.
Why this matters
Train machine learning models strengthens model versioning by feeding better supporting material into the pipeline.
What you get
Supporting assets from train machine learning models are prepared and connected to the main workflow.
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4Core · Step 4 of 7
Open task pageModel Versioning
Execute model versioning with Model Versioning to produce the primary final deliverable.
Why this matters
This is the core step where model versioning actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
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5Refine · Step 5 of 7
Open task pageDeploy machine learning models
Refine and validate model versioning output using Deploy machine learning models before final delivery.
Why this matters
Deploy machine learning models adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
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6Refine · Step 6 of 7
Open task pageTrain AI models
Refine and validate model versioning output using Train AI models before final delivery.
Why this matters
Train AI models adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
7Deliver · Step 7 of 7
Open task pageMonitor model performance
Package and ship the output through Monitor model performance so model versioning reaches end users.
Why this matters
Monitor model performance is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.
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