This page shows the core eval workflow with the Evidently library and links to guides.

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Define and run the eval

To log the evaluation results to the Evidently Platform, first connect to Evidently Cloud or your local workspace and create a Project. It’s optional: you can also run evals locally.

1

Prepare the input data

Get your data in a table like a pandas.DataFrame. More on data requirements. You can also load data from Evidently Platform, like tracing or synthetic datasets.

2

Create a Dataset object

Create a Dataset object with DataDefinition() that specifies column role and types. You can also use default type detection. How to set Data Definition.

eval_data = Dataset.from_pandas(
    pd.DataFrame(source_df),
    data_definition=DataDefinition()
)
3

(Optional) Add descriptors

For text evals, choose and compute row-level descriptors. How to use Descriptors.

eval_data.add_descriptors(descriptors=[
    TextLength("Question", alias="Length"),
    Sentiment("Answer", alias="Sentiment")
])
4

Configure Report

For dataset-level evals (classification, data drift) or to summarize descriptors, create a Report with chosen metrics or presets. How to configure Reports.

report = Report([
    DataSummaryPreset()
])
5

(Optional) Add Test conditions

Add Pass/Fail conditions, like to check if text length in < 100 symbols. How to configure Tests.

report = Report([
    DataSummaryPreset(),
    MaxValue(column="Length", tests=[lt(100)]),
])
6

(Optional) Add Tags and Timestamps

Add tags or metadata to identify specific evaluation runs or datasets, or override the default timestamp . How to add metadata.

7

Run the Report

To execute the eval, runthe Report on the Dataset (or two).

my_eval = report.run(eval_data, None)
8

Explore the results

ws.add_report(project.id, my_eval, include_data=True)

my_eval
##my_eval.json()

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Quickstarts

Check for end-to-end examples:

LLM quickstart

Evaluate the quality of text outputs.

ML quickstart

Test tabular data quality and data drift.

IntroductionData definition