Getting Started with the Data Suitability Recipe
The Data Suitability recipe turns the data feeding an AI model into an AI Use Case, a governance record that binds the model's input assets and quality standard, and then captures an immutable, point-in-time evidence record (an attestation) once the record is certified. It runs in two phases. Define the Use Case names the model, binds its inputs, and confirms the standard its inputs are held to. Capture the Evidence fixes a baseline, records the certified evidence, and hands you the artifact.
Two things shape how this recipe behaves:
- Progress is saved on the AI Use Case itself, not in your browser, so you can close the recipe and continue later from any device.
- Every permission derives from the datastores the use case binds. You need the Author team permission on all of them to build the record, and certifying it takes a Manager with Editor on all of them, or an Admin.
The Deep Dive pages explain how the recipe works, what each step does, what an AI Use Case is, and how to get back into a run. The How-tos walk through each task step by step, and the Troubleshooting, API, and FAQ pages cover the rest.
Permissions
Viewing an AI Use Case needs the Viewer team permission on every datastore it binds. Building one in the recipe needs Author on every bound datastore, and Members can only work on records they created. Certifying or revoking needs the Manager role plus Editor on every bound datastore, or the Admin role. See Permissions for the full matrix.
Deep Dive
Understand what the recipe is, how each phase works, what it writes, and who can do what.
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Introduction
What the recipe is, the two phases, and the governance record it produces.
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How It Works
The two phases, the Use Case picker, how progress is saved on the AI Use Case, and what the recipe writes.
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Define the Use Case
Model, Inputs, and Standard: naming the model, binding its assets, and pinning the check set and expiry schedule.
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Capture the Evidence
Baseline, Evidence, and Complete: fixing the drift referent, certifying, generating the attestation, and exporting it.
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AI Use Cases
The governance record the recipe writes: certification lifecycle, who can do what, attestations, and audit obligations.
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Entry Points and Resuming
Get Started, Open in Recipe, Continue Recipe, Edit in Recipe, and how a run resumes from the AI Use Case.
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Examples
Real scenarios showing a model's inputs turned into a certified evidence record.
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Best Practices
Guidelines for naming, binding scope, baselines, certification, and keeping evidence trustworthy.
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Permissions
The roles, team permissions, and ownership rule behind each step of the recipe.
How-tos
Run the recipe and work with the records it produces, step by step.
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Run the Data Suitability Recipe
Go from the Recipes page to a certified evidence record.
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Resume a Recipe from an AI Use Case
Pick up a saved run from the record list or the AI Use Case page.
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Certify an AI Use Case
Move a record from Draft through review to Certified, with an optional validity date.
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Compare an Attestation with Current Data
Retrieve the attestation current on a date and compare it with today's data health.
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Export an Attestation
Download the evidence record as JSON or as a self-contained HTML report.
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Start a New Baseline
Replace the drift referent and begin a new certification cycle.
Troubleshooting, API and FAQ
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Troubleshooting
Known problems while defining the use case, capturing a baseline, certifying, and generating evidence, and how to resolve them.
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API
The AI Use Case endpoints behind the recipe: records, runs, baselines, certification, attestations, and exports.
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FAQ
Common questions about the record, certification, baselines, attestations, permissions, and resuming.