> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Dashboard Widgets
> Customize dashboards with widgets
## Widget **Overview**
Dashboard widgets are the individual tiles that help create a dashboard view. Widgets provide an easy way to customize dashboards and perform ad hoc analysis. They can be used to build dashboards from scratch, or as a way to modify templated dashboards.
### **Widget Types**
Click in each card to learn more about how to use each widget type
* [Correlate a feature over
time](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#correlate-a-feature-over-time):
compare to a feature/tag value - [Track key slice
performance](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#track-key-slice-performance):
a granular view of model performance
* [Analyze top performing
features](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#analyze-top-performing-features)
with data quality metrics - [Evaluate the heat
map](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#evaluate-heatmap-performance-across-distributions):
Performance across distributions - [Compare predictions vs
actuals](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#compare-predictions-vs-actuals)
* [Highlight key performance
metrics](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#highlight-key-performance-metrics):
for any model type, pair this with time series graphs for a
single-pane-of-glass view of model health
* [Identify gradual changes over
time](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#identify-feature-changes-overtime):
measure drift for any model dimension
* [Visualize sudden model
changes](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#visualize-sudden-model-changes)
* [General
check](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#general-health-check):
combine business-critical monitors across various models in 1 dashboard
* [Annotate
Dashboards](/ax/machine-learning/machine-learning/how-to-ml/dashboards/widgets#annotate-dashboard-with-metadata):
add helpful notes and metadata to share across teams
### Time Series
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Timeseries' widget square
**Define** a the plot by specifying what metric you'd like to see, which Feature / Tag / Actual / Prediction Value you'd like to see first.
**Overlay** important metadata like tags by toggling on "Group metric by feature or tag"
**Enjoy your powerful dashboard view!**
**How to create a widget within a dashboard that shows key slice performance over time**
**Begin** with an existing dashboard, blank dashboard, or start with a [created template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** the Time Series widget creation button
**Define the first plot** by specifying the model, what metric you'd like to see, and the model environment: Production, Pre-production (Validation or Training) and version if applicable.
**Duplicate the plot to quickly start defining your 2nd plot**
**Add a filter to specify the slice in the 2nd plot**
**Success!**
### Distribution
**How to create a widget within a dashboard that analyzes your top performing features**
**Begin** with an existing dashboard, blank dashboard, or start with a [created template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** the Distribution widget creation button
**Define the first plot** by specifying the model, what metric you'd like to see, model environment: Production, Pre-production (Validation or Training), version (if applicable) and what (features, actuals, predictions, etc) you will be displaying the "distribution over".
π‘ This example uses **feature** whose data type is **numeric** **distribution**. If you chose a feature whose data type is **string**, the values will be bucketed by that dimension's values instead of it's numeric ranges.
**Success!**
**How to create a widget within a dashboard where you can evaluate a heatmap of performance across distributions**
**Begin** with an existing dashboard, blank dashboard, or start with a [created template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** the Distribution widget creation button
**Define the base plot** by specifying the model, what metric you'd like to see, model environment: Production, Pre-production (Validation or Training), version (if applicable) and what (features, actuals, predictions, etc) you will be displaying the "distribution over".
π₯**Overlay** performance information by selecting a performance metric in the **Color By** dropdown.
π‘ If the metric requires additional information like Positive Class or at K value, fill out those appropriate fields to get your finalized chart!
**Success!**
**Begin** with an existing dashboard, blank dashboard, or start with a [created template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** the Distribution widget creation button
**Define the first plot** by specifying the model, what metric you'd like to see, model environment: Production, Pre-production (Validation or Training), version (if applicable) and what (features, actuals, predictions, etc) you will be displaying the "distribution over". In this case, we're looking at **Prediction Class**
πΎ**Duplicate the plot in the plot menu to quickly start defining your 2nd plot**
Update the second plot to **Actual Class** to specify what the second plot will be distributing over
π‘ Further narrow down a plot by adding filters to specify problematic features within the main query
**Success!**
### Statistic
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Statistic' widget square
**Define the widget** by specifying the evaluation/performance metric. Then define the rest of the dataset by specifying model, what metric you'd like to see, model environment: Production, Pre-production (Validation or Training), version (if applicable).
π‘ To narrow down on this metric for a given features / actuals / predictions, etc add filters on the primary dataset
**Success!**
### Drift
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Drift' widget square
**Select** the model dimension to measure such as prediction/actual and feature/tag drift
β**Pro Tip:** Gain a more granular view of how each slice impacts your metric by grouping your feature/tag
**Success!**
### Alert Graph
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Alert Graph' widget square
**Select** a prediction drift and feature drift monitors for your model
**Save**, **share**, and **troubleshoot** by clicking the 'View Monitor' link.
β**Pro Tip:** Learn how to troubleshoot drift monitors [here](/ax/machine-learning/machine-learning/how-to-ml/drift-tracing).
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Alert Graph' widget square
**Select** various model monitors that can significantly impact KPIs or are sensitive to change
**Save**, **share**, and **troubleshoo**t by clicking the 'View Monitor' link.
### Text
**Begin** with an existing dashboard, blank dashboard, or start with a created [template](/ax/machine-learning/machine-learning/how-to-ml/dashboards/templates)
**Enter** edit mode
**Select or Drag** 'Text' widget square
**Type** useful notes and other relevant text
**Enjoy your powerful dashboard view!**
Questions? Email us at [support@arize.com](mailto::support@arize.com) or [Slack us](https://arize-ai.slack.com/) in the #arize-support channel