What is Model Stage in Registry: Definition and Usage

A model stage in a registry is a label that shows the current status of a machine learning model, such as Staging, Production, or Archived. It helps teams track and manage models through their lifecycle from testing to deployment and retirement.

How It Works

Think of a model stage like traffic lights for your machine learning models. When a model is first created, it might be in the Staging stage, meaning it is being tested and not yet used for real decisions. Once it passes tests and is ready, it moves to the Production stage, where it actively makes predictions in real-world applications.

Later, if a model becomes outdated or replaced, it can be moved to the Archived stage to keep it safe but inactive. This system helps teams avoid confusion by clearly showing which models are safe to use and which are still being worked on or retired.

Example

This example shows how to register a model and set its stage using Python with MLflow, a popular model registry tool.

python

import mlflow

# Start MLflow client
client = mlflow.tracking.MlflowClient()

# Register a model
model_uri = "runs:/1234567890abcdef/model"
model_name = "MyModel"

# Create a new model version
model_version = client.create_model_version(name=model_name, source=model_uri, run_id=None)

# Transition model stage to 'Staging'
client.transition_model_version_stage(
    name=model_name,
    version=model_version.version,
    stage="Staging"
)

# Check current stage
model_info = client.get_model_version(name=model_name, version=model_version.version)
print(f"Model version {model_info.version} is in stage: {model_info.current_stage}")

Output

Model version 1 is in stage: Staging

When to Use

Use model stages to organize and control your machine learning models as they move from development to real use. For example:

• Staging: When you want to test a new model safely without affecting users.
• Production: When the model is ready and trusted to make real predictions.
• Archived: When a model is no longer used but you want to keep it for records or future reference.

This helps teams avoid mistakes like using untested models in production or losing track of old models.

Key Points

• Model stages label the lifecycle status of machine learning models.
• Common stages include Staging, Production, and Archived.
• Stages help teams manage model deployment safely and clearly.
• Model registries like MLflow provide tools to set and track stages.

Key Takeaways

Model stage shows the current lifecycle status of a machine learning model.

Use stages like Staging and Production to control model deployment safely.

Model registries help track and manage these stages automatically.

Archiving old models keeps your system organized and traceable.