How to Use Model Registry for Deployment in Machine Learning

Use a model registry to store, version, and manage machine learning models centrally. For deployment, retrieve the desired model version from the registry and load it into your serving environment to ensure consistent and trackable model usage.

Syntax

The typical syntax for using a model registry involves registering a model, retrieving it by name and version, and then loading it for deployment.

• register_model(model_uri, name): Save the model with a unique name.
• get_model(name, version): Retrieve the model from the registry.
• load_model(model_uri): Load the model into memory for inference or deployment.

python

from mlflow import register_model
from mlflow.pyfunc import load_model

# Register a model
register_model(model_uri='runs:/1234567890abcdef/model', name='my_model')

# Retrieve model URI
model_uri = f'models:/my_model/1'

# Load model for deployment
model = load_model(model_uri=model_uri)

Example

This example shows how to register a simple scikit-learn model, then load it from the model registry for deployment.

python

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Start MLflow run
with mlflow.start_run() as run:
    # Log model
    mlflow.sklearn.log_model(model, artifact_path="model")
    run_id = run.info.run_id

# Register the model
model_uri = f"runs:/{run_id}/model"
model_name = "IrisRFModel"
mlflow.register_model(model_uri, model_name)

# Load the registered model for deployment
from mlflow.pyfunc import load_model
loaded_model = load_model(f"models:/{model_name}/1")

# Predict with loaded model
predictions = loaded_model.predict(X_test)
print(predictions[:5])

Output

[1 0 2 1 1]

Common Pitfalls

Common mistakes when using a model registry for deployment include:

• Not specifying the correct model version, leading to loading the wrong model.
• Failing to register the model after training, so the deployment environment cannot find it.
• Using inconsistent model URIs or names, causing errors when loading.
• Not handling model stage transitions (e.g., from Staging to Production), which can cause deploying untested models.

python

## Wrong way: Loading without version
# model = load_model(f"models:/IrisRFModel")  # Missing version causes error

## Right way: Specify version explicitly
# model = load_model(f"models:/IrisRFModel/1")

Quick Reference

Action	Command/Method	Description
Register Model	mlflow.register_model(model_uri, name)	Save model with a name and version in registry
Load Model	mlflow.pyfunc.load_model('models:/name/version')	Load model for inference or deployment
List Models	mlflow.client().list_registered_models()	View all registered models
Transition Stage	mlflow.client().transition_model_version_stage()	Change model stage (e.g., Staging to Production)

Key Takeaways

Always register your trained model with a unique name and version in the model registry.

Load models for deployment using the exact model name and version to avoid errors.

Manage model stages (Staging, Production) to control deployment readiness.

Use the model registry to track and reproduce deployed models easily.

Avoid loading models without specifying versions to prevent unexpected behavior.