Leverage all the Galileo 'building blocks' that are logged and stored for you to create Tests using Galileo Conditions -- a class for building custom data quality checks.
Conditions are simple and flexible, allowing you to author powerful data/model tests.
Run Report
Integrate with email or slack to automatically receive a report of Condition outcomes after a run finishes processing.
Examples
Example 1: Alert if over 50% of high DEP (>=0.7) data contains PII
>>> c = Condition(
... operator=Operator.gt,
... threshold=0.5,
... agg=AggregateFunction.pct,
... filters=[
... ConditionFilter(
... metric="data_error_potential", operator=Operator.gte, value=0.7
... ),
... ConditionFilter(
... metric="galileo_pii", operator=Operator.neq, value="None"
... ),
... ],
... )
>>> dq.register_run_report(conditions=[c])
Example 2: Alert if at least 20% of the dataset has drifted (Inference DataFrames only)
>>> c = Condition(
... operator=Operator.gte,
... threshold=0.2,
... agg=AggregateFunction.pct,
... filters=[
... ConditionFilter(
... metric="is_drifted", operator=Operator.eq, value=True
... ),
... ],
... )
>>> dq.register_run_report(conditions=[c])
⚡️ Get started building your own Reports with Galileo Conditions
