Creación de un pipeline de alertas de drift con Evidently AI
Usará la biblioteca Evidently para generar un informe HTML de drift de datos, integrar la comprobación en un script programado y activar una alerta por correo electrónico al detectar drift.
Creación de un pipeline de alertas de drift con Evidently AI es una lección gratuita de Machine Learning Academy en CoddyKit. Esta es la lección 4 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Machine Learning Academy, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Machine Learning Academy incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
What Is Evidently AI?
Evidently AI is an open-source Python library for evaluating and monitoring machine learning models in production. It provides pre-built reports and test suites covering data drift, data quality, model performance, and prediction drift. With a few lines of code, Evidently generates interactive HTML reports or JSON summaries that can be embedded in monitoring dashboards or sent as email attachments, making it the fastest way to add comprehensive monitoring to an existing ML pipeline.
# Install Evidently
# pip install evidently
import evidently
print('Evidently version:', evidently.__version__)
# Evidently organises monitoring into:
# 1. Reports -- exploratory HTML dashboards
# 2. Test Suites -- pass/fail checks for CI/CD integration
# 3. Metrics -- individual measurements (DataDriftPreset, etc.)
from evidently.report import Report
from evidently.test_suite import TestSuite
from evidently.metric_preset import DataDriftPreset, DataQualityPreset
print('Imports successful.')Preparing Reference and Current Datasets
Evidently compares a reference dataset (typically the training data or a recent stable production period) against a current dataset (the most recent production batch). Both must be pandas DataFrames with the same column schema. The reference establishes the baseline; the current is checked for drift relative to that baseline. Include the target column if ground-truth labels are available; Evidently will use them for performance metrics.
import pandas as pd
import numpy as np
from sklearn.datasets import make_classification
np.random.seed(42)
# Simulate training data (reference)
X_ref, y_ref = make_classification(n_samples=5000, n_features=8, random_state=42)
reference = pd.DataFrame(X_ref, columns=[f'feature_{i}' for i in range(8)])
reference['target'] = y_ref
# Simulate production batch with some drift
X_cur = X_ref[:1000].copy()
X_cur[:, 2] += 2.0 # shift feature_2
X_cur[:, 5] *= 1.8 # scale feature_5
current = pd.DataFrame(X_cur, columns=[f'feature_{i}' for i in range(8)])
current['target'] = y_ref[:1000] # labels may or may not be available
print('Reference shape:', reference.shape)
print('Current shape:', current.shape)Generating a Data Drift Report
The DataDriftPreset generates a comprehensive drift report covering all features. It automatically selects the appropriate statistical test for each column (KS test for continuous features, chi-squared for categorical) and reports the drift score, p-value, and a yes/no drift decision for each. The HTML output includes interactive visualisations showing the reference vs current distribution for each drifted feature.
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
# Build and run the report
data_drift_report = Report(metrics=[DataDriftPreset()])
data_drift_report.run(
reference_data=reference,
current_data=current
)
# Save to HTML file
data_drift_report.save_html('/tmp/data_drift_report.html')
print('Report saved to /tmp/data_drift_report.html')
# Extract as dictionary for programmatic use
result_dict = data_drift_report.as_dict()
drift_summary = result_dict['metrics'][0]['result']
print('Dataset drift detected:', drift_summary['dataset_drift'])
print('Features drifted:', drift_summary['number_of_drifted_columns'])Column Drift Details
Access per-column drift results programmatically to build automated alerts. Evidently returns a dictionary with each column's drift score, statistical test used, p-value, and a boolean drift flag. Filter for drifted columns and include them in your alert message to help the team immediately understand which features have changed and by how much.
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=reference, current_data=current)
result = report.as_dict()['metrics'][0]['result']
print('Per-column drift summary:')
for col, info in result['drift_by_columns'].items():
drift_flag = info.get('drift_detected', False)
stat_name = info.get('stattest_name', 'N/A')
p_val = info.get('p_value', 'N/A')
status = '*** DRIFTED ***' if drift_flag else 'stable'
print(f' {col:12s}: {stat_name} p={p_val:.4f} [{status}]' if isinstance(p_val, float)
else f' {col}: {status}')Data Quality Report
Beyond drift, Evidently's DataQualityPreset checks for data quality issues in the current batch: missing values, constant columns, duplicates, and out-of-range values. These issues are often the first sign of an upstream data pipeline failure rather than true drift. Running data quality checks before drift checks prevents false drift alarms caused by missing imputation or schema changes in the ETL pipeline.
from evidently.report import Report
from evidently.metric_preset import DataQualityPreset
import pandas as pd
import numpy as np
# Introduce some quality issues
current_with_issues = current.copy()
current_with_issues.loc[50:80, 'feature_0'] = np.nan # missing values
current_with_issues.loc[100:110, 'feature_3'] = 99999 # outliers
quality_report = Report(metrics=[DataQualityPreset()])
quality_report.run(
reference_data=reference,
current_data=current_with_issues
)
quality_report.save_html('/tmp/data_quality_report.html')
quality_dict = quality_report.as_dict()
print('Quality report generated with', len(quality_dict['metrics']), 'metrics.')Test Suites for CI/CD: Pass or Fail
Reports are for humans; test suites are for machines. A test suite runs a battery of configurable checks and returns a pass/fail result that can gate pipeline steps. If any test fails, the pipeline raises an exception, the CI/CD step fails, and the alert fires. Common tests: number of drifted columns below threshold, missing value rate below threshold, all columns within expected value ranges.
from evidently.test_suite import TestSuite
from evidently.tests import (
TestNumberOfDriftedColumns,
TestShareOfMissingValues,
TestNumberOfConstantColumns
)
test_suite = TestSuite(tests=[
TestNumberOfDriftedColumns(lt=2), # fail if > 1 column drifts
TestShareOfMissingValues(lt=0.01), # fail if >1% missing
TestNumberOfConstantColumns(lt=1) # fail if any constant column
])
test_suite.run(reference_data=reference, current_data=current)
test_suite.save_html('/tmp/test_suite_report.html')
result = test_suite.as_dict()
summary = result['summary']
print(f'Tests passed: {summary["passed_tests"]}/{summary["total_tests"]}')
print('Overall status:', 'PASS' if summary['all_passed'] else 'FAIL')Integrating Evidently into a Scheduled Script
Automate drift monitoring by running Evidently on a daily cron job. The script loads the previous day's production batch, compares it to the reference dataset, runs the test suite, and sends an alert if tests fail. This pattern requires no ML expertise to maintain — the scheduled script runs silently when everything is healthy and raises an alarm only when action is needed.
import pandas as pd
from evidently.test_suite import TestSuite
from evidently.tests import TestNumberOfDriftedColumns, TestShareOfMissingValues
from datetime import date
def daily_drift_check(reference_path, production_path, alert_threshold=2):
reference = pd.read_parquet(reference_path)
current = pd.read_parquet(production_path)
suite = TestSuite(tests=[
TestNumberOfDriftedColumns(lt=alert_threshold),
TestShareOfMissingValues(lt=0.05)
])
suite.run(reference_data=reference, current_data=current)
result = suite.as_dict()
passed = result['summary']['all_passed']
report_path = f'/tmp/drift_{date.today()}.html'
suite.save_html(report_path)
return passed, report_path, result['summary']
# In cron script:
# passed, report, summary = daily_drift_check('data/reference.parquet', 'data/today.parquet')
# if not passed:
# send_email_alert(report, summary)
print('daily_drift_check function ready.')Sending Email Alerts on Drift Detection
When the test suite fails, send an email alert with the HTML report attached. Use Python's built-in smtplib and email modules to compose a message with the drift summary in the body and the full Evidently HTML report as an attachment. The recipient can open the report to see exactly which features drifted and by how much, without needing access to the production environment.
import smtplib
import os
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
def send_drift_alert(recipient, report_path, summary):
smtp_host = os.getenv('SMTP_HOST', 'smtp.gmail.com')
smtp_user = os.getenv('SMTP_USER')
smtp_pass = os.getenv('SMTP_PASS')
msg = MIMEMultipart()
msg['From'] = smtp_user
msg['To'] = recipient
msg['Subject'] = '[ML ALERT] Data drift detected in production'
body = (f'Drift alert triggered.\n'
f'Tests passed: {summary["passed_tests"]}/{summary["total_tests"]}\n'
f'See attached HTML report for details.')
msg.attach(MIMEText(body, 'plain'))
with open(report_path, 'rb') as f:
attachment = MIMEApplication(f.read(), _subtype='html')
attachment.add_header('Content-Disposition', 'attachment',
filename='drift_report.html')
msg.attach(attachment)
print(f'Alert email sent to {recipient}')
print('send_drift_alert function ready.')Slack Webhook Integration
For teams that use Slack, posting a concise drift alert to a dedicated #ml-monitoring channel is more immediately visible than email. Include the number of drifted features, the most severely drifted column, and a link to the saved HTML report in cloud storage (S3 or GCS). Use Slack Block Kit for formatted messages with colour-coded status (green = healthy, red = drift detected).
import requests
import json
import os
def post_slack_alert(summary, drifted_columns, report_url):
webhook_url = os.getenv('SLACK_WEBHOOK_URL')
if not webhook_url:
print('No Slack webhook configured.')
return
all_passed = summary['all_passed']
icon = ':white_check_mark:' if all_passed else ':rotating_light:'
color = 'good' if all_passed else 'danger'
status_text = 'All checks passed' if all_passed else 'DRIFT DETECTED'
message = {
'attachments': [{
'color': color,
'title': f'{icon} ML Monitoring Alert',
'text': (f'Status: {status_text}\n'
f'Drifted features: {drifted_columns}\n'
f'Tests: {summary["passed_tests"]}/{summary["total_tests"]} passed\n'
f'<{report_url}|View full report>'),
'fallback': status_text
}]
}
response = requests.post(webhook_url, data=json.dumps(message))
print('Slack response:', response.status_code)Saving Reports to S3 for Audit Trail
Store every generated Evidently report in S3 or GCS with a date-stamped key to create a permanent audit trail. This enables retrospective analysis: when a model's performance drops in April, retrieve the March drift report to see whether drift was already visible weeks earlier. Configure an S3 lifecycle policy to retain reports for 2 years and automatically archive older ones to Glacier storage for cost efficiency.
import boto3
import os
from datetime import date
def upload_report_to_s3(local_path, bucket, model_name):
s3 = boto3.client(
's3',
aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY')
)
today = date.today().isoformat()
s3_key = f'ml-monitoring/{model_name}/{today}/drift_report.html'
s3.upload_file(
local_path,
bucket,
s3_key,
ExtraArgs={'ContentType': 'text/html'}
)
report_url = f'https://{bucket}.s3.amazonaws.com/{s3_key}'
print(f'Report uploaded: {report_url}')
return report_url
print('upload_report_to_s3 function ready.')Dashboard Integration with Grafana
Evidently produces HTML reports suitable for human review, but for operational monitoring you need a real-time dashboard that updates automatically. Extract PSI and KS statistics as numeric values from Evidently test results, push them to Prometheus (via the prometheus_client library), and visualise in Grafana. This gives your SRE team the same observability tooling for ML models as for application services, enabling drift alerts alongside latency and error-rate alerts in one place.
# Export Evidently metrics to Prometheus
from prometheus_client import Gauge, start_http_server
import time
# Define Prometheus gauges for drift metrics
psi_gauge = Gauge('ml_feature_psi', 'Population Stability Index', ['feature_name'])
ks_gauge = Gauge('ml_feature_ks_statistic', 'KS test statistic', ['feature_name'])
# Push computed drift values (call after running Evidently tests)
def push_drift_metrics(drift_results: dict):
for feature, metrics in drift_results.items():
psi_gauge.labels(feature_name=feature).set(metrics.get('psi', 0))
ks_gauge.labels(feature_name=feature).set(metrics.get('ks_stat', 0))
# start_http_server(8000) # Prometheus scrapes this port
print('Expose metrics at :8000/metrics -> scrape in Prometheus -> display in Grafana')Quick Check
Test your understanding of Machine Learning with Python concepts from this lesson.
Lesson Recap
In this lesson you learned: Evidently AI generates comprehensive drift and data quality reports with a few lines of Python, Test Suites provide pass/fail results that integrate directly into CI/CD pipelines and scheduled cron jobs, and combining S3 storage, email, and Slack notifications creates a complete automated alert pipeline that catches drift before it visibly impacts users. Next up we enter the Responsible AI section, starting with SHAP values for interpreting global and local feature importance in complex models.
Preguntas frecuentes
¿La lección «Creación de un pipeline de alertas de drift con Evidently AI» es gratis?
Sí — el texto completo de «Creación de un pipeline de alertas de drift con Evidently AI» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Machine Learning Academy, actualiza a CoddyKit PRO. El curso de Machine Learning Academy incluye 4 lecciones en total.
¿Qué aprenderé en «Creación de un pipeline de alertas de drift con Evidently AI»?
Usará la biblioteca Evidently para generar un informe HTML de drift de datos, integrar la comprobación en un script programado y activar una alerta por correo electrónico al detectar drift. Practicas Machine Learning Academy con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar Machine Learning Academy?
No se requiere experiencia previa. Machine Learning Academy en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 4 de 4.
¿Cuánto tiempo toma la lección «Creación de un pipeline de alertas de drift con Evidently AI»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de Machine Learning Academy?
Sí. Cada lección de Machine Learning Academy incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Desplazamiento de datos: cambios en la distribución de características con el tiempo
- Desplazamiento de concepto: cuando cambia la relación entre X e Y
- Supervisión de distribuciones de predicciones y puntuaciones de confianza
- Creación de un pipeline de alertas de drift con Evidently AI