evidently-drift-detector

Evidently Drift Detector

Detect data drift, monitor model performance, and generate automated reports using Evidently AI.

Overview

This skill provides comprehensive capabilities for ML monitoring using Evidently AI. It enables detection of data drift, concept drift, target drift, and model performance degradation in production ML systems.

Capabilities

Data Drift Detection

• Feature-level drift detection
• Dataset-level drift analysis
• Multiple drift detection methods (KS, PSI, Wasserstein, etc.)
• Distribution visualization
• Drift magnitude quantification

Model Performance Monitoring

• Classification metrics tracking
• Regression metrics tracking
• Performance degradation detection
• Slice-based analysis
• Error analysis

Target Drift Analysis

• Target distribution changes
• Label drift detection
• Prediction drift monitoring
• Class balance monitoring

Automated Reporting

• HTML report generation
• JSON metrics export
• Dashboard integration
• Custom metric creation
• Test suite execution

Production Monitoring

• Real-time monitoring integration
• Alerting threshold configuration
• Time-series drift tracking
• Batch comparison analysis

Prerequisites

Installation

pip install evidently>=0.4.0

Optional Dependencies

# For Spark support
pip install evidently[spark]

# For specific visualizations
pip install plotly nbformat

Usage Patterns

Basic Data Drift Report

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

# Define column mapping
column_mapping = ColumnMapping(
    target='target',
    prediction='prediction',
    numerical_features=['feature_1', 'feature_2', 'feature_3'],
    categorical_features=['category_1', 'category_2']
)

# Create drift report
report = Report(metrics=[
    DataDriftPreset()
])

# Run report comparing reference and current data
report.run(
    reference_data=reference_df,
    current_data=current_df,
    column_mapping=column_mapping
)

# Save report
report.save_html("drift_report.html")

# Get metrics as dictionary
metrics_dict = report.as_dict()

Classification Performance Report

from evidently.metric_preset import ClassificationPreset

report = Report(metrics=[
    ClassificationPreset()
])

report.run(
    reference_data=reference_df,
    current_data=current_df,
    column_mapping=column_mapping
)

# Access specific metrics
results = report.as_dict()
accuracy = results['metrics'][0]['result']['current']['accuracy']

Regression Performance Report

from evidently.metric_preset import RegressionPreset

report = Report(metrics=[
    RegressionPreset()
])

report.run(
    reference_data=reference_df,
    current_data=current_df,
    column_mapping=column_mapping
)

Test Suite for Automated Checks

from evidently.test_suite import TestSuite
from evidently.test_preset import DataDriftTestPreset, DataQualityTestPreset

# Create test suite
test_suite = TestSuite(tests=[
    DataDriftTestPreset(),
    DataQualityTestPreset()
])

# Run tests
test_suite.run(
    reference_data=reference_df,
    current_data=current_df,
    column_mapping=column_mapping
)

# Check results
if test_suite.as_dict()['summary']['all_passed']:
    print("All tests passed!")
else:
    failed_tests = [t for t in test_suite.as_dict()['tests'] if t['status'] == 'FAIL']
    print(f"Failed tests: {len(failed_tests)}")

Individual Drift Metrics

from evidently.metrics import (
    DatasetDriftMetric,
    ColumnDriftMetric,
    DataDriftTable,
    TargetByFeaturesTable
)

# Detailed drift analysis
report = Report(metrics=[
    DatasetDriftMetric(),
    ColumnDriftMetric(column_name='feature_1'),
    ColumnDriftMetric(column_name='feature_2'),
    DataDriftTable(),
    TargetByFeaturesTable()
])

report.run(
    reference_data=reference_df,
    current_data=current_df,
    column_mapping=column_mapping
)

Custom Drift Thresholds

from evidently.metrics import DatasetDriftMetric
from evidently.options import DataDriftOptions

# Custom options
options = DataDriftOptions(
    drift_share=0.5,  # Share of drifted features to flag dataset drift
    stattest='psi',   # Statistical test
    stattest_threshold=0.1  # PSI threshold
)

report = Report(metrics=[
    DatasetDriftMetric(options=options)
])

Time-Series Monitoring

import pandas as pd
from datetime import datetime, timedelta

def monitor_over_time(reference_df, production_data_stream, window_days=7):
    """Monitor drift over time windows."""
    results = []

    for window_start in production_data_stream:
        window_end = window_start + timedelta(days=window_days)
        current_window = production_data_stream.query(
            f"timestamp >= '{window_start}' and timestamp < '{window_end}'"
        )

        report = Report(metrics=[DataDriftPreset()])
        report.run(reference_data=reference_df, current_data=current_window)

        metrics = report.as_dict()
        results.append({
            'window_start': window_start,
            'drift_detected': metrics['metrics'][0]['result']['dataset_drift'],
            'drift_share': metrics['metrics'][0]['result']['drift_share']
        })

    return pd.DataFrame(results)

Integration with Babysitter SDK

Task Definition Example

const driftDetectionTask = defineTask({
  name: 'evidently-drift-detection',
  description: 'Detect data drift between reference and current data',

  inputs: {
    referenceDataPath: { type: 'string', required: true },
    currentDataPath: { type: 'string', required: true },
    targetColumn: { type: 'string' },
    predictionColumn: { type: 'string' },
    numericalFeatures: { type: 'array' },
    categoricalFeatures: { type: 'array' },
    driftThreshold: { type: 'number', default: 0.5 }
  },

  outputs: {
    driftDetected: { type: 'boolean' },
    driftShare: { type: 'number' },
    driftedFeatures: { type: 'array' },
    reportPath: { type: 'string' }
  },

  async run(inputs, taskCtx) {
    return {
      kind: 'skill',
      title: 'Detect data drift',
      skill: {
        name: 'evidently-drift-detector',
        context: {
          operation: 'detect_drift',
          referenceDataPath: inputs.referenceDataPath,
          currentDataPath: inputs.currentDataPath,
          targetColumn: inputs.targetColumn,
          predictionColumn: inputs.predictionColumn,
          numericalFeatures: inputs.numericalFeatures,
          categoricalFeatures: inputs.categoricalFeatures,
          driftThreshold: inputs.driftThreshold
        }
      },
      io: {
        inputJsonPath: `tasks/${taskCtx.effectId}/input.json`,
        outputJsonPath: `tasks/${taskCtx.effectId}/result.json`
      }
    };
  }
});

Available Presets

Metric Presets

| Preset | Use Case |

|--------|----------|

| DataDriftPreset | Feature drift detection |

| DataQualityPreset | Data quality checks |

| ClassificationPreset | Classification model performance |

| RegressionPreset | Regression model performance |

| TargetDriftPreset | Target variable drift |

| TextOverviewPreset | Text data analysis |

Test Presets

| Preset | Use Case |

|--------|----------|

| DataDriftTestPreset | Automated drift tests |

| DataQualityTestPreset | Data quality validation |

| DataStabilityTestPreset | Data stability checks |

| NoTargetPerformanceTestPreset | Proxy performance tests |

| RegressionTestPreset | Regression performance tests |

| MulticlassClassificationTestPreset | Multiclass tests |

| BinaryClassificationTestPreset | Binary classification tests |

Statistical Tests Available

| Test | Method | Best For |

|------|--------|----------|

| ks | Kolmogorov-Smirnov | Numerical, general |

| psi | Population Stability Index | Production monitoring |

| wasserstein | Wasserstein distance | Distribution comparison |

| jensenshannon | Jensen-Shannon divergence | Probability distributions |

| chisquare | Chi-square | Categorical features |

| z | Z-test | Large samples, normal |

| kl_div | KL divergence | Information theory |

ML Pipeline Integration

Retraining Trigger

def check_retraining_needed(reference_df, current_df, column_mapping, threshold=0.3):
    """Determine if model retraining is needed based on drift."""
    from evidently.test_suite import TestSuite
    from evidently.tests import TestShareOfDriftedColumns

    test_suite = TestSuite(tests=[
        TestShareOfDriftedColumns(lt=threshold)
    ])

    test_suite.run(
        reference_data=reference_df,
        current_data=current_df,
        column_mapping=column_mapping
    )

    results = test_suite.as_dict()
    retraining_needed = not results['summary']['all_passed']

    return {
        'retraining_needed': retraining_needed,
        'drift_share': results['tests'][0]['result']['current'],
        'threshold': threshold
    }

Performance Degradation Alert

def check_performance_degradation(reference_df, current_df, column_mapping, min_accuracy=0.85):
    """Alert if classification accuracy drops below threshold."""
    from evidently.tests import TestAccuracyScore

    test_suite = TestSuite(tests=[
        TestAccuracyScore(gte=min_accuracy)
    ])

    test_suite.run(
        reference_data=reference_df,
        current_data=current_df,
        column_mapping=column_mapping
    )

    results = test_suite.as_dict()
    return {
        'degradation_detected': not results['summary']['all_passed'],
        'current_accuracy': results['tests'][0]['result']['current'],
        'threshold': min_accuracy
    }

Best Practices

1. Establish Baselines: Use production data as reference, not training data
2. Choose Appropriate Tests: Match statistical tests to data types
3. Set Meaningful Thresholds: Balance sensitivity vs. alert fatigue
4. Monitor Feature Importance: Focus on high-impact features
5. Time-Based Comparison: Compare similar time periods
6. Document Decisions: Record why certain drift is acceptable

References

• Evidently AI Documentation
• Evidently GitHub
• ML Monitoring Course
• Evidently Blog