0Pricing
Machine Learning Academy · 课时

使用 Evidently AI 构建数据漂移告警流水线

学习者将使用 Evidently 库生成数据漂移 HTML 报告，将检查集成到定时脚本中，并在检测到漂移时触发电子邮件告警。

使用 Evidently AI 构建数据漂移告警流水线 是 CoddyKit 上的免费 Machine Learning Academy 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Machine Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Machine Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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.

常见问题解答

「使用 Evidently AI 构建数据漂移告警流水线」课时是免费的吗？

是的 — 「使用 Evidently AI 构建数据漂移告警流水线」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Machine Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Machine Learning Academy 课程共包含 4 节课。

「使用 Evidently AI 构建数据漂移告警流水线」这节课中我会学到什么？

学习者将使用 Evidently 库生成数据漂移 HTML 报告，将检查集成到定时脚本中，并在检测到漂移时触发电子邮件告警。 你通过在浏览器中直接运行的动手代码来练习 Machine Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Machine Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Machine Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。

「使用 Evidently AI 构建数据漂移告警流水线」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Machine Learning Academy 课中编写并运行代码吗？

能。每节 Machine Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 数据漂移：特征分布随时间发生变化
  2. 概念漂移：X 与 Y 之间的关系发生变化
  3. 监控预测分布与置信度分数
  4. 使用 Evidently AI 构建数据漂移告警流水线
← 返回 Machine Learning Academy