| name | register-ml-model |
| description | Register, version, and govern MLflow models through a full lifecycle from training-run artifact to production stage with validation gates, alias-based deployment routing, lineage tracking, and CI/CD automation. Triggers on "register a model", "promote model to production", "manage model versions", "model stage transitions", "model governance", "model rollback", "model lineage", and "champion/challenger A/B models". |
Register ML Model
Register MLflow-trained models to the Model Registry for systematic versioning, stage management, and deployment governance.
When to use
- Promoting a trained model from experimentation to production
- Managing multiple model versions across development stages
- Implementing model approval workflows for governance
- Tracking model lineage from training to deployment
- Rolling back to previous model versions
- Comparing deployed model versions for A/B testing
- Auditing model changes for compliance requirements
- Setting up champion/challenger model patterns
When NOT to use
- Model training is still in progress (use
track-ml-experiments instead)
- The model has not been logged to MLflow with
mlflow.*.log_model()
- You only need file-based artifact storage without versioning (use direct artifact logging)
- Your MLflow server runs SQLite backend (Model Registry requires PostgreSQL or MySQL)
- You need real-time model serving without registry abstraction (deploy directly from artifacts)
- The use case involves one-off inference without reproducibility requirements
Procedure
Step 1: Verify Model Registry Backend
Confirm the MLflow server has a database-backed Model Registry (PostgreSQL or MySQL; SQLite is not supported).
Verify backend connectivity:
import mlflow
from mlflow.tracking import MlflowClient
MLFLOW_TRACKING_URI = "http://mlflow-server.company.com:5000"
mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
client = MlflowClient()
try:
registered_models = client.search_registered_models()
print(f"Model Registry available ({len(registered_models)} models)")
except Exception as e:
raise RuntimeError(f"Model Registry not available: {e}")
Server startup (PostgreSQL backend):
mlflow server \
--backend-store-uri postgresql://user:pass@localhost:5432/mlflow \
--default-artifact-root s3://mlflow-artifacts/models \
--host 0.0.0.0 \
--port 5000
Expected: client.search_registered_models() returns list (may be empty), MLflow UI shows "Models" tab, database has registered_models table.
On failure:
- Check MLflow version:
mlflow --version (must be ≥1.2)
- Verify database backend is PostgreSQL/MySQL (SQLite unsupported for registry)
- Check server logs:
journalctl -u mlflow-server or Docker logs
- Test connection:
curl http://mlflow-server:5000/api/2.0/mlflow/registered-models/list
- Verify database permissions:
psql -h localhost -U mlflow_user -c "\dt" should show mlflow tables
Failure Handling
| Failure Mode | Diagnosis | Resolution |
|---|
MlflowException: Model Registry not available | SQLite backend in use | Switch to PostgreSQL/MySQL backend |
AttributeError: 'MlflowClient' has no attribute 'search_registered_models' | MLflow version < 1.20 | Upgrade MLflow: pip install --upgrade mlflow |
| Model registers but artifact download fails | Artifact storage unreachable | Verify default-artifact-root URI and network access |
SearchModelVersion returns empty on existing model | Wrong model name or case mismatch | Use exact model_name as registered; names are case-sensitive |
Stage transition raises MlflowException | Version already in target stage or concurrent transition in progress | Check client.get_model_version(name, version).current_stage; retry after current transition completes |
Alias assignment raises AttributeError | MLflow < 2.0 | Fall back to tag-based alias: client.set_model_version_tag(name, version, f"alias_{alias}", "true") |
| Tag value exceeds 5000 character limit | Lineage metadata too large | Truncate or split into multiple tags |
| Rollback finds no archived version | Previous Production was not properly archived | Ensure archive_existing_versions=True on every promotion; inspect client.search_model_versions() for orphaned versions |
Validation
Per-Step Verification
Step 1: client.search_registered_models() returns list (may be empty), MLflow UI shows "Models" tab, database has registered_models table.
Step 2: Model appears in Registry UI with assigned version, accessible via models:/customer-churn-classifier/1, tags and description populated.
Step 3: Model version stage updates in Registry UI, previous Staging/Production versions archived, transition timestamps in tags.
Step 4: Aliases appear in Registry UI, models:/name@alias URI resolves, alias updates immediately affect new loads.
Step 5: Model version tags contain lineage data, JSON-serializable values stored as strings, get_model_lineage() returns structured dictionary.
Step 6: Workflow triggers via GitHub UI or API, validation passes, model stage updates, deployment infrastructure receives event.
Output Contract
| Artifact | Method | Format |
|---|
| Registered model | mlflow.register_model() | ModelVersion object |
| Stage transition | client.transition_model_version_stage() | Updates in-place, returns None |
| Alias assignment | client.set_registered_model_alias() | Updates in-place, returns None |
| Lineage record | client.get_model_version() + client.get_run() | dict with nested dicts |
| Promotion event | CI/CD workflow dispatch | GitHub Actions workflow_dispatch |
Common Pitfalls
- SQLite limitations: Model Registry requires database backend (PostgreSQL/MySQL) for production - file-based registry causes concurrency issues
- Stage conflicts: Multiple versions in same stage cause confusion - use
archive_existing_versions=True to auto-archive
- Missing run linkage: Registering models without run_id loses lineage - always register from MLflow runs, not raw files
- Alias confusion: Using stages as deployment targets instead of aliases - stages are for workflow, aliases for deployment references
- Validation skipped: Promoting to Production without checks - implement mandatory validation in CI/CD pipeline
- No rollback plan: Production issues without rollback capability - maintain previous Production version in Archived stage
- Tag overload: Too many unstructured tags - standardize tag schema and naming conventions
- Manual processes: Human-driven promotions are error-prone and slow - automate with CI/CD and approval workflows
- Lost artifacts: Model registered but artifacts deleted from storage - ensure artifact retention policies align with model lifecycle
Related Skills
track-ml-experiments - Log models to MLflow before registering themdeploy-ml-model-serving - Deploy registered models to serving infrastructurerun-ab-test-models - A/B test models using registry aliasesorchestrate-ml-pipeline - Automate model training and registrationversion-ml-data - Version training data for model lineage
