Core concepts

Reference and Current Data

The primary use for Evidently is the comparison between two datasets. These datasets are model application logs. They can include model input features, predictions, and/or actuals (true labels).

  • Reference dataset serves as a basis for the comparison.

  • Current (Production) dataset is the dataset that is compared to the first.

In practice, you can use it in different combinations:

  • Training vs Test

    • To compare the model performance on a hold-out Test to the Training.

    • Pass the training data as "Reference", and test data as "Current".

  • Production vs Training

    • To compare the Production model performance to the Training period.

    • Pass the training data as "Reference", and production data as "Current".

  • Current perfromance vs Past

    • To compare the Current production performance to an Earlier period.

    • For example, to compare the last week to the previous week or month.

    • Pass the earlier data as "Reference", and newer data as "Current".

  • Compare any two models or datasets

    • For example, to estimate the historical drift for different windows in your training data or to compare how two models perform in the test.

    • Pass the first dataset as "Reference", and the second as "Current".

You can generate a Performance report for a single dataset. Pass it as "Reference". In other cases, we need two datasets to run the statistical tests.

Right now, you cannot choose a custom name for your dataset.

Note: earlier, we referred to the second dataset as "Production". You might notice that in some older examples.

Reports

Evidently includes a set of pre-built Reports. Each of them addresses a specific aspect of the data or model performance.

Evidently calculates a number of metrics and statistical tests in each report, and generates interactive visualizations.

Currently, you can choose between 6 different Report types.

The calculation results can be available in one of the following formats:

  • An interactive visual Dashboard displayed inside the Jupyter notebook.

  • An HTML report. Same as dashboard, but available as a standalone file.

  • A JSON profile. A summary of the metrics, the results of statistical tests, and simple histograms.

Right now, you cannot change the composition of the report, e.g. to add or exclude metrics. Reports are pre-built to serve as good enough defaults. We expect to add configurations in the future.

Dashboards

To display the output in the Jupyter notebook, you can create a visual Dashboard.

To specify which analysis you want to perform, you should select a Tab (for example, a Data Drift tab). You can combine several tabs in a single Dashboard (for example, Data Drift and Prediction Drift). Each tab will contain a combination of metrics, interactive plots, and tables for a chosen Report type.

You can also save the Dashboard as a standalone HTML file. You can group several Tabs in one file.

You can generate HTML files from Jupyter notebook or using Terminal.

This option helps visually explore and evaluate model performance and errors.

JSON Profiles

To get the calculation results as a JSON file, you should create a Profile.

To specify which analysis you want to perform, you should select a Section. You can combine several sections together in a single Profile. Each section will contain a summary of metrics, results of statistical tests, and simple histograms that correspond to the chosen Report type.

You can generate profiles from Jupyter notebook or using Terminal.

This option helps integrate Evidently in your prediction pipelines: for example, you can use Evidently to calculate drift, and then log the needed metrics externally (e.g. using Mlflow).

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