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Dashboard Panels

TruEra Monitoring Dashboards are comprised of panels, each helping you to visualize and track a different aspect and/or metric for the production models included in the dashboard configuration.


Over the course of a production model deployment, a number of

Machine Learning Operations

AI/ML engineering discipline focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them.
conditions can arise, signaling an unexpected change in certain model results and/or an impact on the usefulness of model predictions.

This could include but isn't limited to:

  • Shifts in the underlying data distribution, causing the model to "go stale."
  • Edge cases in the production datastream not encountered during model development.
  • Deployed model misconfiguration.

None of which stops the model from making "successful" predictions, at least from a service perspective, but the predictions are less likely to be useful.

TruEra Monitoring's dashboard panels help you detect and identify anomalous model behavior before there are costly business repurcussions, so you can take prompt action — actions that can range from triggering a model retraining or adjusting/updating the production datasource, all the way to taking the model offline for further investigation.

By now, you should have installed the TruEra client, ingested your model and production datastore, and created a dashboard to help track model output.

Next, let's take a deeper look at each available dashboard panel and the monitoring information it reports.

To display the panels for a selected dashboard:

  1. With the Monitoring tab open, select a Dashboard by clicking on its row.
  2. The default for Dashboard Views is Model. Scroll down to see each of your pre-configured panels (see Creating/Changing a Dashboard for guidance).
  3. Switch between Model and Data views by clicking the respective control in the left-hand navigator.

switch dashboard views

Return to your dashboard master list at any time by clicking ← Back to Monitoring.

Panel exploration in the next topic follows the structure in the table below. Click a panel link to quickly visit its description.

Panel TypeRegression Model PanelsClassification Model Panels
Model OutputMean
Predictions and Labels
Model Drift: Difference of Means
Model Drift: Wasserstein
Model Drift: Difference of Means
Model Drift: Wasserstein
Decisions and Labels by Class
Class Distribution
Model Score Distribution
LabelsLabel Volume
Label Distribution
Label Drift
Label Volume
Label Class Distributions
Model Performance
(requires labels)
Data InputInput Volume
Data Drift: Difference of Means
Data Drift: Wasserstein
Out of Range Values
Data Quality
(tabular data only)
Unrecognized Categorical
Numerical Issues
Schema Mismatch
Missing Values
DQ Exploration
CustomSegment Performance
Custom Level Metrics
  – General
  – Model
  – Record

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