register-ml-model

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:

# model_registry_config.py
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()

# Verify registry is accessible
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:

1. Check MLflow version: mlflow --version (must be ≥1.2)
2. Verify database backend is PostgreSQL/MySQL (SQLite unsupported for registry)
3. Check server logs: journalctl -u mlflow-server or Docker logs
4. Test connection: curl http://mlflow-server:5000/api/2.0/mlflow/registered-models/list
5. 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 them
• deploy-ml-model-serving - Deploy registered models to serving infrastructure
• run-ab-test-models - A/B test models using registry aliases
• orchestrate-ml-pipeline - Automate model training and registration
• version-ml-data - Version training data for model lineage