Evidently AI: Monitor ML Models for Data Drift and Performance Degradation

Evidently AI generates data drift reports, quality checks, and model performance dashboards for production ML — catching distribution shifts before they silently corrupt your predictions.

Mahmudul Haque Qudrati

CEO & ML Engineer

May 1, 2026

7 min read

// tags

#evidently-ai#ml-monitoring#data-drift#model-monitoring#production

FIG. ART-26

7 min read

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Evidently AI: Monitor ML Models for Data Drift and Performance Degradation

// reading plan

sections

352

words

min read

// Machine Learning

ONNX: Export Any ML Model and Run It Anywhere

ONNX (Open Neural Network Exchange) is the universal model format — export from PyTorch, scikit-learn, or HuggingFace and run 3x faster inference with ONNX Runtime on CPU or GPU.

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// Machine Learning

Decision Trees and Random Forests Explained: When Tree Methods Beat Neural Networks

Why ML Models Degrade in Production

A model trained in January on clean data may perform poorly by June because the real world changed. Three kinds of drift cause this:

Data drift: input feature distributions shift (e.g., your price feature starts ranging 0-1000 instead of 0-100).

Concept drift: the relationship between features and labels changes (e.g., user behavior that predicted churn no longer does).

Target drift: the label distribution shifts (e.g., fraud rate changes from 2% to 8%).

Evidently AI detects all three automatically.

Generating a Data Drift Report

import pandas as pd
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset, DataQualityPreset

# Load reference (training) and current (production) data
reference = pd.read_parquet("reference_data.parquet")
current = pd.read_parquet("current_data.parquet")

report = Report(metrics=[
    DataDriftPreset(),
    DataQualityPreset(),
])

report.run(reference_data=reference, current_data=current)
report.save_html("data_drift_report.html")

The HTML report shows statistical tests (KS test for numerical, chi-square for categorical) for each feature, drift severity, and distribution visualizations.

Classification Model Performance Report

from evidently.metric_preset import ClassificationPreset

report = Report(metrics=[ClassificationPreset()])
report.run(
    reference_data=reference_df,  # must have target and prediction columns
    current_data=current_df,
    column_mapping=ColumnMapping(
        target="label",
        prediction="predicted_label",
        prediction_probas=["prob_0", "prob_1"],
    )
)
report.save_html("classification_report.html")

Test Suite for Automated Pass/Fail

Reports are for humans. Test Suites are for pipelines:

from evidently.test_suite import TestSuite
from evidently.tests import (
    TestShareOfDriftedColumns,
    TestColumnDrift,
    TestNumberOfMissingValues,
)

tests = TestSuite(tests=[
    TestShareOfDriftedColumns(lt=0.2),             # fail if >20% columns drift
    TestColumnDrift(column_name="user_age"),         # fail if age column drifts
    TestNumberOfMissingValues(lt=1000),              # fail if >1000 missing values
])

tests.run(reference_data=reference, current_data=current)

if not tests.as_dict()["summary"]["all_passed"]:
    raise ValueError("Data quality check failed — investigate before retraining")

Integrating with Airflow

from airflow import DAG
from airflow.operators.python import PythonOperator

def run_drift_check():
    # load data, run tests, raise on failure
    ...

with DAG("daily_drift_check", schedule_interval="@daily") as dag:
    drift_check = PythonOperator(
        task_id="check_drift",
        python_callable=run_drift_check,
    )

    retrain = PythonOperator(
        task_id="retrain_model",
        python_callable=trigger_retraining,
        trigger_rule="all_failed",  # retrain only if drift check failed
    )

    drift_check >> retrain

Evidently vs WhyLogs vs Fiddler

| | Evidently | WhyLogs | Fiddler | |---|---|---|---| | Open source | Yes | Yes | No (SaaS) | | Self-host | Yes | Yes | No | | Reports | Rich HTML | Basic | Rich (managed) | | Real-time | Evidently Cloud | Yes (WhyLabs) | Yes | | Price | Free / Cloud | Free / WhyLabs | $$$|

Resources: Evidently GitHub, docs, Evidently Cloud.

Evidently AI: Monitor ML Models for Data Drift and Performance Degradation

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ONNX: Export Any ML Model and Run It Anywhere

Decision Trees and Random Forests Explained: When Tree Methods Beat Neural Networks

Why ML Models Degrade in Production

Generating a Data Drift Report

Classification Model Performance Report

Test Suite for Automated Pass/Fail

Integrating with Airflow

Evidently vs WhyLogs vs Fiddler

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Evidently AI: Monitor ML Models for Data Drift and Performance Degradation

Related Articles

ONNX: Export Any ML Model and Run It Anywhere

Decision Trees and Random Forests Explained: When Tree Methods Beat Neural Networks

Why ML Models Degrade in Production

Generating a Data Drift Report

Classification Model Performance Report

Test Suite for Automated Pass/Fail

Integrating with Airflow

Evidently vs WhyLogs vs Fiddler

The workspace your teamactually needs

AI & ML insights, weekly

Mahmudul Haque Qudrati

Supervised Learning Explained: How Models Learn from Labeled Examples

The workspace your team
actually needs