MLFlow - Problem with aliases and metrics

What you’re seeing is expected MLflow behavior, and it can be confusing at first in Fabric.

Key point: in MLflow, metrics/params live on the Run, not on the Model Registry objects. A ModelVersion (or ModelInfo, LoggedModel, etc.) typically exposes registry metadata (name, version, tags, source, run_id), but not the run’s params/metrics directly. The right pattern is:

ModelVersion → run_id → client.get_run(run_id)

If aliases are failing in Fabric, it’s usually because the hosted registry endpoint doesn’t support the alias APIs (or the underlying MLflow server feature set differs). In that case, a practical workaround is to use model version tags as a “pseudo-alias” (e.g., champion=true, env=prod) and resolve your “production” version via tags.

References:

• MLflow Client / Registry API: https://mlflow.org/docs/latest/python_api/mlflow.client.html

• MLflow Model Registry (aliases & versions): https://mlflow.org/docs/latest/ml/model-registry/ and https://mlflow.org/docs/latest/ml/model-registry/tutorial/

• Fabric ML model experience (run tracking + registering): https://learn.microsoft.com/en-us/fabric/data-science/machine-learning-model
  
  
  

import mlflow
from mlflow.tracking import MlflowClient

client = MlflowClient()

model_name = "<YOUR_MODEL_NAME>"
model_version = "<YOUR_VERSION_NUMBER>"  # e.g. "3"

# 1) Registry -> ModelVersion
mv = client.get_model_version(name=model_name, version=model_version)
print("Model:", mv.name, "Version:", mv.version, "RunId:", mv.run_id)

# 2) Run -> params/metrics
run = client.get_run(mv.run_id)

print("\nParams:")
print(run.data.params)

print("\nMetrics:")
print(run.data.metrics)

# Optional: metric history (if you logged multiple values over time)
# hist = client.get_metric_history(mv.run_id, "accuracy")
# print(hist)