Question 1

Explanation

Correct Answer:
✅ mlflow.update_model_version

Explanation:

In MLflow Model Registry, the update_model_version() method is used to update metadata of an existing model version, such as:

• Changing its description

• Adding or modifying tags

• Updating other metadata fields

This allows data scientists to manage model documentation and tracking efficiently without re-registering a new version.

Example: Updating a Model Version’s Metadata in MLflow

import mlflow
from mlflow.tracking import MlflowClient
 
# Initialize MLflow Client
client = MlflowClient()
 
# Define model details
model_name = "my_model"
model_version = 2  # The model version to update
 
# Update model description
client.update_model_version(
    name=model_name,
    version=model_version,
    description="Updated model description: Improved accuracy and retrained on new dataset."
)
 
# Add a new tag to the model version
client.set_model_version_tag(
    name=model_name,
    version=model_version,
    key="dataset_version",
    value="v2.1"
)
 
print(f"Updated metadata for {model_name} version {model_version}")

• update_model_version() updates the model version's description.

• set_model_version_tag() adds tags for better tracking.

Why Other Options Are Incorrect?

1. mlflow.update_model_metadata

   • Incorrect, because no such function exists in MLflow.

   • Metadata updates are done using update_model_version() instead.

2. mlflow.register_model

   • Incorrect, because registering a model creates a new model version, rather than updating an existing one.

3. mlflow.edit_model_version

   • Incorrect, because this function does not exist in MLflow.

   • The correct function is update_model_version().

Final Answer:

✅ Use mlflow.update_model_version() to modify metadata, descriptions, and tags for an existing MLflow model version.