MLflow Model Registry

Why Model Registry?

In production, you need to know: - Which model version is currently serving? - Who approved it for production? - What was the previous version (for rollback)? - How does the new version compare to the old one?

MLflow Model Registry answers all of these.

Registering a Model

After training and logging a model:

import mlflow

with mlflow.start_run():
    # Train your model...
    mlflow.sklearn.log_model(
        model,
        "model",
        registered_model_name="wine-quality-classifier"
    )

Or register an existing run:

result = mlflow.register_model(
    "runs:/<run-id>/model",
    "wine-quality-classifier"
)

Model Stages

MLflow supports lifecycle stages:

• None — just registered
• Staging — being tested
• Production — serving live traffic
• Archived — retired but preserved

Transitioning Stages

from mlflow import MlflowClient

client = MlflowClient()

# Move to staging
client.transition_model_version_stage(
    name="wine-quality-classifier",
    version=2,
    stage="Staging"
)

# Promote to production
client.transition_model_version_stage(
    name="wine-quality-classifier",
    version=2,
    stage="Production"
)

Loading Models by Stage

# Load the production model
model = mlflow.sklearn.load_model(
    "models:/wine-quality-classifier/Production"
)

# Load a specific version
model = mlflow.sklearn.load_model(
    "models:/wine-quality-classifier/2"
)

Model Descriptions and Tags

Add context to model versions:

client.update_model_version(
    name="wine-quality-classifier",
    version=2,
    description="Tuned with RandomizedSearchCV, accuracy: 0.94"
)

client.set_model_version_tag(
    name="wine-quality-classifier",
    version=2,
    key="approved_by",
    value="luca"
)

Best Practices

1. Always register production models — no anonymous models in prod
2. Add descriptions — future you will thank present you
3. Use staging — test before promoting
4. Keep archived versions — rollback is critical
5. Automate transitions — CI/CD can handle stage changes

Integrating with Kubernetes

The Model Registry becomes even more powerful when combined with Kubernetes deployments. Automate the pipeline: register → stage → test → promote → deploy. Learn how in our MLflow for Kubernetes course.

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Ready to go deeper? Check out our hands-on course: MLflow for Kubernetes — practical exercises you can follow along on your own machine.