Who should use the Develop machine learning models 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 pageDevelop AI models
Prepare inputs and settings through Develop AI models before running develop machine learning models.
Why this matters
Develop AI models sets up the foundation for develop machine learning models; 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 7
Open task pageTrain machine learning models
Use Train machine learning models to build supporting assets that improve develop machine learning models quality.
Why this matters
Train machine learning models strengthens develop machine learning models 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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3Input · Step 3 of 7
Open task pageTrain AI models
Use Train AI models to build supporting assets that improve develop machine learning models quality.
Why this matters
Train AI models strengthens develop machine learning models by feeding better supporting material into the pipeline.
What you get
Supporting assets from train ai models are prepared and connected to the main workflow.
4Core · Step 4 of 7
Open task pageDevelop machine learning models
Execute develop machine learning models with Develop machine learning models to produce the primary final deliverable.
Why this matters
This is the core step where develop machine learning models 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 pageTrain deep learning models
Refine and validate develop machine learning models output using Train deep learning models before final delivery.
Why this matters
Train deep 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 pageEvaluate AI Models
Refine and validate develop machine learning models output using Evaluate AI Models before final delivery.
Why this matters
Evaluate 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.
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7Deliver · Step 7 of 7
Open task pageBuild AI Models
Package and ship the output through Build AI Models so develop machine learning models reaches end users.
Why this matters
Build AI Models 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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