How do I set up a model catalog for governance workflows and role-based access across our ML team?

A good model catalog is both a system of record and a workflow layer for governance. The goal is to make every model discoverable, versioned, reviewable, and access-controlled—without slowing the ML team down.

1) Define the catalog’s purpose

Start by deciding what the catalog must support:

• Discovery: find models by owner, use case, domain, status, risk, or metric
• Governance: approvals, reviews, sign-off history, audit trail
• Access control: who can view, edit, approve, deploy, or retire models
• Lineage: data sources, training code, experiments, artifacts, deployments
• Operational readiness: SLA, monitoring, drift, incidents, rollback info

A strong catalog usually tracks both:

• Model metadata: business + technical information
• Governance metadata: policy, approvals, and controls

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2) Define a standard model record

Create a required schema for every model entry. Example fields:

Business fields

• Model name
• Description / intended use
• Business owner
• Technical owner
• Team / squad
• Use case / product
• Department / domain
• Status: draft, in review, approved, deployed, retired

Technical fields

• Model type: classifier, regressor, LLM, recommender, etc.
• Framework / library
• Training dataset(s)
• Feature store / source systems
• Metrics: AUC, F1, RMSE, latency, cost
• Evaluation date
• Deployment target / endpoint
• Model version
• Artifact location

Governance fields

• Risk tier: low / medium / high
• PII / sensitive data usage
• Regulatory relevance: SOC 2, HIPAA, GDPR, etc.
• Required approvers
• Approval status
• Review dates
• Exceptions / waivers
• Audit log links

Operational fields

• Monitoring dashboards
• Drift thresholds
• Incident runbooks
• Rollback procedure
• Owner on-call / escalation contact

Make some fields mandatory before a model can move to “approved” or “deployed.”

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3) Establish role-based access control (RBAC)

Define roles clearly. A common pattern is:

Typical roles

• Viewer: can search and view approved metadata
• Contributor: can create/edit draft model entries
• Reviewer: can comment, request changes, and review evidence
• Approver: can approve for deployment or production use
• Publisher/Deployer: can trigger deployment after approval
• Admin: manages taxonomy, permissions, policies
• Auditor/Compliance: read-only access to full history and evidence

Permission examples

Action	Viewer	Contributor	Reviewer	Approver	Admin
View model metadata	✓	✓	✓	✓	✓
Edit draft metadata		✓			✓
Submit for review		✓			✓
Comment / request changes			✓	✓	✓
Approve / reject				✓	✓
Deploy				maybe	✓
Change policies / roles					✓
View audit history	✓	✓	✓	✓	✓

For larger organizations, use group-based access rather than individual permissions:

• Data Science Team
• ML Platform Team
• Risk/Compliance Team
• Product Owners
• External Auditors

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4) Build a governance workflow

A simple model governance flow could be:

1. Create draft entry
2. Attach evidence
   • training data info
   • experiment results
   • fairness/bias analysis
   • security review
   • privacy review
3. Automated checks
   • required fields complete
   • ownership assigned
   • policy checks pass
   • thresholds met
4. Human review
   • technical review
   • risk/compliance review if required
   • business approval
5. Approve and publish
6. Deploy
7. Monitor
8. Periodic re-certification
9. Retire / archive

A model can only move forward if prerequisites are met. For example:

• high-risk models require legal/compliance approval
• models using sensitive data require privacy review
• production models require monitoring dashboard links

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5) Use policy-based controls for flexibility

RBAC is useful, but governance often needs more nuance. Add policy-based access rules such as:

• Only the model owner group can edit draft entries
• Only approvers in the same business domain can approve
• Compliance can view all high-risk models
• Production deployment requires approval from both ML lead and risk owner
• High-risk models must be reviewed every 90 days

This prevents over-permissioning while keeping workflows smooth.

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6) Separate public metadata from restricted evidence

Not every user should see everything.

Public/internal catalog view

• model name
• owner
• status
• purpose
• approved metrics
• deployment environment

Restricted evidence

• raw training data details
• feature definitions with sensitive context
• security findings
• legal reviews
• incident reports
• PII references

Use object-level permissions or separate document storage with secure links if needed.

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7) Integrate the catalog with your ML stack

The catalog becomes much more useful if it auto-ingests metadata from tools you already use:

• CI/CD: deployment status, commit hashes
• Experiment tracking: metrics from MLflow, W&B, etc.
• Feature store: feature lineage
• Data platform: dataset versions
• Model registry: model versions and stages
• Monitoring: drift, latency, error rates
• Identity provider: groups and SSO for RBAC

Automate as much as possible so teams don’t manually fill everything in.

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8) Set governance gates by risk tier

Don’t use one-size-fits-all controls.

Low-risk models

• required metadata
• owner assigned
• basic review
• monitoring enabled

Medium-risk models

• documentation
• peer review
• production approval
• monitoring + alerts
• scheduled recertification

High-risk models

• formal approval workflow
• fairness/bias review
• privacy/security assessment
• audit log retention
• periodic re-approval
• stricter access to evidence

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9) Create auditability from day one

You’ll want a full history of:

• who created/edited each record
• who approved it and when
• what version was approved
• what changed between versions
• when it was deployed/retired
• exceptions granted and by whom

This is critical for compliance and internal accountability.

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10) Recommended operating model

A practical ownership model is:

• ML Platform team: platform, schemas, integrations, permissions
• Model owners: keep records current
• Domain leads: approve use-case readiness
• Risk/Compliance: define policy and review high-risk models
• Security/Privacy: review sensitive-data use
• Admins: manage catalog taxonomy and access

Set a policy that model owners must update catalog records at key lifecycle events:

• after training
• before release
• after incident
• after retraining
• at scheduled review

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11) Start with a minimum viable catalog

If you want to roll this out quickly, implement these first:

1. Required model metadata schema
2. SSO-based RBAC with 4–6 roles
3. Approval workflow for production models
4. Audit logs
5. Automated ingestion from your model registry/experiment tracker
6. Basic search/filtering
7. Periodic review reminders

Then expand into risk scoring, policy automation, and deeper lineage.

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12) A simple implementation blueprint

If you’re designing this from scratch, a common architecture is:

• Catalog UI: search, detail pages, review workflow
• Metadata store: relational DB or metadata service
• Identity/RBAC layer: SSO + group mapping
• Workflow engine: approvals, tasks, notifications
• Policy engine: rules for access and gating
• Integration jobs: sync from ML tools and data systems
• Audit log store: immutable history

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13) Questions to answer before implementation

• Who is the system of record for model metadata?
• What model states do we support?
• Which approvals are required for each risk tier?
• Who can see sensitive evidence?
• What systems are the sources of truth for metrics and lineage?
• How will stale records be detected?
• Who owns access reviews and permission changes?

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If you want, I can also provide:

1. a sample model catalog schema,
2. an RBAC matrix, or
3. a workflow diagram for governance approvals.