Concept | Metrics

Metrics are measurements on datasets, folders, models, or model evaluation stores. They allow for monitoring the current status and evolution of these Dataiku items. For example, we could compute:

• The number of records in a dataset

• The size of a folder

• The accuracy of a model

Metrics on datasets

The Metrics tab within datasets includes several default metrics, such as the column count and record count. You can also edit the metrics to include:

• Column statistics such as sum, average, minimum, maximum, etc.

• Most frequent values of columns, such as the mode or top N values

• Column percentiles

• Data validity, or checking that the records match with a column’s Meaning. (Note that the dataset’s meaning must be locked or manually selected for this metric to be applied.)

• Custom metrics calculated using a formula or code

Because there can be a lot of available metrics on an item, you can select the metrics you want to add to the displayed screen tiles on the Metrics page.

Note

You can also create custom metrics using a Python probe or SQL probe. For more information and examples, see the Knowledge Base concept or the Developer Guide.

Metrics on other Flow objects

In addition to datasets, you can also set up metrics on other Flow objects:

• Folders

• Models

• Model evaluation stores

For example, you can compute metrics in the Status tab of a managed folder to see the count of included files and size of the folder.

Monitoring metrics

You can use metrics to monitor the value of a metric in combination with checks or data quality rules.

Object	Feature for monitoring
Datasets	For Dataiku 12.6 or later, use data quality rules. (Note that data quality rules don’t need to be built on top of metrics, but they can be.) For Dataiku 12.5 and before, use checks.
Folders, models, or model evaluation stores	For all versions of Dataiku, use checks.