evidently 0.4.19

Open-source tools to analyze, monitor, and debug machine learning model in production.

Evidently

An open-source framework to evaluate, test and monitor ML models in production.

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:new: New release

Evidently 0.4.0. Self-host an ML Monitoring interface -> QuickStart

:bar_chart: What is Evidently?

Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings.

Evidently has a modular approach with 3 components on top of the shared metrics functionality.

1. Tests: batch model checks

Tests perform structured data and ML model checks. They verify a condition and return an explicit pass or fail.

You can create a custom Test Suite from 50+ tests or run a preset (for example, Data Drift or Regression Performance). You can get results as a JSON, Python dictionary, exportable HTML, visual report inside Jupyter notebook, or as Evidently JSON snapshot.

Tests are best for automated checks. You can integrate them as a pipeline step using tools like Airflow.

2. Reports: interactive visualizations

Old dashboards API was deprecated in v0.1.59. Here is the migration guide.

Reports calculate various data and ML metrics and render rich visualizations. You can create a custom Report or run a preset to evaluate a specific aspect of the model or data performance. For example, a Data Quality or Classification Performance report.

You can get an HTML report (best for exploratory analysis and debugging), JSON or Python dictionary output (best for logging, documentation or to integrate with BI tools), or as Evidently JSON snapshot.

3. ML monitoring dashboard

This functionality is available from v0.4.0.

You can self-host an ML monitoring dashboard to visualize metrics and test results over time. This functionality sits on top of Reports and Test Suites. You must store their outputs as Evidently JSON snapshots that serve as a data source for the Evidently Monitoring UI.

You can track 100+ metrics available in Evidently, from the number of nulls to text sentiment and embedding drift.

:woman_technologist: Install Evidently

MAC OS and Linux

Evidently is available as a PyPI package. To install it using pip package manager, run:

pip install evidently

Evidently is also available on Anaconda distribution platform. To install Evidently using conda installer, run:

conda install -c conda-forge evidently

Windows

Evidently is available as a PyPI package. To install it using pip package manager, run:

pip install evidently

To install Evidently using conda installer, run:

conda install -c conda-forge evidently

:arrow_forward: Getting started

Option 1: Test Suites

This is a simple Hello World example. Head to docs for a complete Quickstart for Reports and Test Suites.

Prepare your data as two pandas DataFrames. The first is your reference data, and the second is current production data. The structure of both datasets should be identical. You need input features only to run some evaluations (e.g., Data Drift). In other cases (e.g., Target Drift, Classification Performance), you need Target and/or Prediction.

After installing the tool, import the Evidently Test Suite and required presets. We'll use a simple toy dataset:

import pandas as pd

from sklearn import datasets

from evidently.test_suite import TestSuite
from evidently.test_preset import DataStabilityTestPreset
from evidently.test_preset import DataQualityTestPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

To run the Data Stability Test Suite and display the output in the notebook:

data_stability= TestSuite(tests=[
    DataStabilityTestPreset(),
])
data_stability.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_stability 

You can also save an HTML file. You'll need to open it from the destination folder.

data_stability.save_html("file.html")

To get the output as JSON:

data_stability.json()

Option 2: Reports

After installing the tool, import the Evidently Report and required presets:

import pandas as pd

from sklearn import datasets

from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

To generate the Data Drift report, run:

data_drift_report = Report(metrics=[
    DataDriftPreset(),
])

data_drift_report.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_drift_report

Save the report as HTML. You'll later need to open it from the destination folder.

data_drift_report.save_html("file.html")

To get the output as JSON:

data_drift_report.json()

Option 3: ML monitoring dashboard

This will launch a demo project in the Evidently UI. Head to docs for a complete ML Monitoring Quickstart.

Recommended step: create a virtual environment and activate it.

pip install virtualenv
virtualenv venv
source venv/bin/activate

After installing Evidently (pip install evidently), run the Evidently UI with the demo projects:

evidently ui --demo-projects all

Access Evidently UI service in your browser. Go to the localhost:8000.

:computer: Contributions

We welcome contributions! Read the Guide to learn more.

:books: Documentation

For more information, refer to a complete Documentation. You can start with the tutorials:

• Get Started with Reports and Test Suites
• Get Started with ML monitoring

:card_index_dividers: Examples

Simple examples on toy datasets to quickly explore what Evidently can do out of the box.

Title	Code example	Tutorial	Contents
QuickStart Tutorial: ML Monitoring	Example	Tutorial	Pre-built ML monitoring dashboard.
QuickStart Tutorial: Tests and Reports	Jupyter notebook Colab	Tutorial	Data Stability and custom Test Suites, Data Drift and Target Drift Reports
Evidently Metric Presets	Jupyter notebook Colab	-	Data Drift, Target Drift, Data Quality, Regression, Classification Reports
Evidently Metrics	Jupyter notebook Colab	-	All individual Metrics
Evidently Test Presets	Jupyter notebook Colab	-	NoTargetPerformance, Data Stability, Data Quality, Data Drift Regression, Multi-class Classification, Binary Classification, Binary Classification top-K test suites
Evidently Tests	Jupyter notebook Colab	-	All individual Tests

There are more example in the Community Examples repository.

Integrations

Explore Integrations to see how to integrate Evidently in the prediction pipelines and with other tools.

How-to guides

Explore the How-to guides to understand specific features in Evidently, such as working with text data.

:phone: User Newsletter

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