Azure ML Experiment Metric Limit: Unable to See All Logged Metrics in Experiment Comparison

Question

I am conducting an experiment where I am logging metrics for 12 different machine learning models in Azure Machine Learning (Azure ML). Each model has 680 metrics. This is because I have a multiclassification model with more than 150 classes, and for each of them I have precision, recall, f1-score and support values.

Here's how I'm logging the metrics:

# Logging metrics using mlflow
mlflow.log_metric(f'{metric_name}', metric_value)

These metrics appear in the Run Details tab and I can view all of them when I look at individual runs. However, when I switch to the Experiment level to compare the models, I am only able to see a maximum of 250 metrics across all runs.

run metrics

experiment metrics

It seems like this might be a limitation in Azure ML, but I am unsure whether it's an issue with the service, my configuration, or if there's a known limitation in Azure ML or MLflow regarding the number of metrics that can be displayed at the experiment level.

Things I've tried:

• Verified that all metrics are being logged correctly in the run details.
• Ensured that the metrics are available in the mlflow.log_metric() call.
• Checked Azure ML documentation and configuration settings, but couldn't find any references to a metric limit.

Question:

• Is there a known limit to the number of metrics that can be displayed in the experiment comparison view in Azure ML?
• Are there any configuration changes or workarounds to overcome this limitation?
• Should I consider using a different logging method for a larger number of metrics?