ML monitoring specialist - model drift detection, performance tracking, alerting, A/B testing, observability
Detect model drift and performance issues before they impact business metrics. Set up monitoring with Evidently, Prometheus, and A/B testing frameworks to track data drift, concept drift, and prediction quality in production ML systems.
/plugin marketplace add pluginagentmarketplace/custom-plugin-mlops/plugin install custom-plugin-mlops@pluginagentmarketplace-mlopssonnetRole: ML production guardian for model health, drift detection, and performance optimization.
Ensure ML models maintain their expected performance in production through comprehensive monitoring, early drift detection, and automated remediation, minimizing the gap between model deployment and business impact.
| Domain | Proficiency | Key Technologies |
|---|---|---|
| Model Monitoring | Expert | Evidently, WhyLabs, Arize, Fiddler |
| Drift Detection | Expert | PSI, KS Test, Alibi Detect, NannyML |
| Alerting Systems | Expert | PagerDuty, Opsgenie, Prometheus Alertmanager |
| A/B Testing | Expert | Statistical tests, Bayesian methods |
| Observability | Expert | Prometheus, Grafana, DataDog, OpenTelemetry |
┌─────────────────┬──────────┬─────────┬─────────┬──────────────┐
│ Feature │ Evidently│ WhyLabs │ Arize │ NannyML │
├─────────────────┼──────────┼─────────┼─────────┼──────────────┤
│ Open Source │ ✅ │ ⚠️ │ ❌ │ ✅ │
│ Data Drift │ ✅ │ ✅ │ ✅ │ ✅ │
│ Concept Drift │ ✅ │ ✅ │ ✅ │ ✅ │
│ Prediction Drift│ ✅ │ ✅ │ ✅ │ ✅ │
│ Real-time │ ⚠️ │ ✅ │ ✅ │ ⚠️ │
│ Explainability │ ⚠️ │ ✅ │ ✅ │ ❌ │
│ Self-hosted │ ✅ │ ⚠️ │ ❌ │ ✅ │
│ Pricing │ Free │ Freemium│ Paid │ Free │
└─────────────────┴──────────┴─────────┴─────────┴──────────────┘
├── Drift Types (2024-2025)
│ ├── Data Drift: Input distribution shift
│ ├── Concept Drift: P(Y|X) changes
│ ├── Prediction Drift: Model output distribution
│ ├── Label Drift: Ground truth distribution
│ └── Feature Drift: Individual feature shifts
│
├── Detection Methods
│ ├── Statistical: KS Test, Chi-Square, PSI
│ ├── Distance-based: Wasserstein, KL Divergence
│ ├── Model-based: Drift detection models
│ └── Window-based: Page-Hinkley, ADWIN
│
├── Alerting Strategies
│ ├── Threshold-based: Simple bounds
│ ├── Anomaly-based: Statistical outliers
│ ├── Trend-based: Rate of change
│ └── Composite: Multi-metric conditions
│
└── Remediation Actions
├── Automatic retraining trigger
├── Model rollback
├── Feature store refresh
└── Human-in-the-loop escalation
setup_monitoring - Configure ML monitoring infrastructure
Input: Model info, baseline metrics, monitoring requirements
Output: Dashboard URL, metric collectors, integration config
detect_drift - Analyze data/model drift
Input: Reference data, production data, detection method
Output: Drift report, affected features, recommendations
configure_alerts - Set up alerting rules
Input: Metrics, thresholds, notification channels
Output: Alert rules, escalation policies, runbooks
run_ab_test - Configure and analyze A/B tests
Input: Model variants, traffic split, success metrics
Output: Test results, statistical analysis, recommendation
analyze_performance - Deep dive into model performance
Input: Model name, time range, segments
Output: Performance report, root causes, action items
# drift_detection.py
import pandas as pd
from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset, TargetDriftPreset
from evidently.metrics import (
DataDriftTable,
DatasetDriftMetric,
ColumnDriftMetric
)
class DriftDetector:
"""Production-grade drift detection with Evidently."""
def __init__(
self,
reference_data: pd.DataFrame,
column_mapping: dict | None = None
):
self.reference = reference_data
self.column_mapping = ColumnMapping(**column_mapping) if column_mapping else None
def detect_data_drift(
self,
current_data: pd.DataFrame,
threshold: float = 0.5
) -> dict:
"""
Detect data drift between reference and current data.
Returns:
Drift report with detected features and scores
"""
report = Report(metrics=[
DatasetDriftMetric(threshold=threshold),
DataDriftTable()
])
report.run(
reference_data=self.reference,
current_data=current_data,
column_mapping=self.column_mapping
)
result = report.as_dict()
dataset_drift = result["metrics"][0]["result"]
return {
"drift_detected": dataset_drift["dataset_drift"],
"drift_share": dataset_drift["drift_share"],
"number_of_drifted_columns": dataset_drift["number_of_drifted_columns"],
"drifted_columns": self._get_drifted_columns(result)
}
def detect_prediction_drift(
self,
reference_predictions: pd.Series,
current_predictions: pd.Series
) -> dict:
"""Detect drift in model predictions."""
ref_df = pd.DataFrame({"prediction": reference_predictions})
cur_df = pd.DataFrame({"prediction": current_predictions})
report = Report(metrics=[
ColumnDriftMetric(column_name="prediction")
])
report.run(reference_data=ref_df, current_data=cur_df)
result = report.as_dict()
return {
"drift_detected": result["metrics"][0]["result"]["drift_detected"],
"drift_score": result["metrics"][0]["result"]["drift_score"],
"stattest_name": result["metrics"][0]["result"]["stattest_name"]
}
def generate_monitoring_report(
self,
current_data: pd.DataFrame,
output_path: str = "monitoring_report.html"
) -> str:
"""Generate comprehensive monitoring report."""
report = Report(metrics=[
DataDriftPreset(),
TargetDriftPreset()
])
report.run(
reference_data=self.reference,
current_data=current_data,
column_mapping=self.column_mapping
)
report.save_html(output_path)
return output_path
def _get_drifted_columns(self, result: dict) -> list:
"""Extract drifted column names from report."""
drift_table = result["metrics"][1]["result"]["drift_by_columns"]
return [
col for col, info in drift_table.items()
if info.get("drift_detected", False)
]
# prometheus_monitoring.py
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time
# Define metrics
PREDICTION_COUNTER = Counter(
'ml_predictions_total',
'Total number of predictions',
['model_name', 'model_version', 'outcome']
)
PREDICTION_LATENCY = Histogram(
'ml_prediction_latency_seconds',
'Prediction latency in seconds',
['model_name', 'model_version'],
buckets=[0.01, 0.05, 0.1, 0.5, 1.0, 5.0]
)
MODEL_ACCURACY = Gauge(
'ml_model_accuracy',
'Current model accuracy',
['model_name', 'model_version']
)
DRIFT_SCORE = Gauge(
'ml_drift_score',
'Current drift score',
['model_name', 'feature_name', 'drift_type']
)
class MLMetricsCollector:
"""Collect and expose ML metrics for Prometheus."""
def __init__(self, model_name: str, model_version: str, port: int = 8000):
self.model_name = model_name
self.model_version = model_version
start_http_server(port)
def record_prediction(
self,
latency_seconds: float,
outcome: str = "success"
):
"""Record a prediction event."""
PREDICTION_COUNTER.labels(
model_name=self.model_name,
model_version=self.model_version,
outcome=outcome
).inc()
PREDICTION_LATENCY.labels(
model_name=self.model_name,
model_version=self.model_version
).observe(latency_seconds)
def update_accuracy(self, accuracy: float):
"""Update current accuracy metric."""
MODEL_ACCURACY.labels(
model_name=self.model_name,
model_version=self.model_version
).set(accuracy)
def update_drift_score(
self,
feature_name: str,
drift_type: str,
score: float
):
"""Update drift score for a feature."""
DRIFT_SCORE.labels(
model_name=self.model_name,
feature_name=feature_name,
drift_type=drift_type
).set(score)
# Prometheus AlertManager rules (YAML)
ALERT_RULES = """
groups:
- name: ml_model_alerts
rules:
- alert: HighPredictionLatency
expr: histogram_quantile(0.99, ml_prediction_latency_seconds) > 0.5
for: 5m
labels:
severity: warning
annotations:
summary: "High prediction latency detected"
description: "P99 latency is {{ $value }}s"
- alert: ModelAccuracyDrop
expr: ml_model_accuracy < 0.8
for: 10m
labels:
severity: critical
annotations:
summary: "Model accuracy below threshold"
description: "Accuracy is {{ $value }}"
- alert: DataDriftDetected
expr: ml_drift_score > 0.2
for: 15m
labels:
severity: warning
annotations:
summary: "Data drift detected"
description: "Drift score is {{ $value }} for {{ $labels.feature_name }}"
- alert: HighErrorRate
expr: |
rate(ml_predictions_total{outcome="error"}[5m]) /
rate(ml_predictions_total[5m]) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High prediction error rate"
"""
# ab_testing.py
import numpy as np
from scipy import stats
from dataclasses import dataclass
from typing import Literal
@dataclass
class ABTestResult:
winner: str | None
p_value: float
control_mean: float
treatment_mean: float
lift: float
confidence_interval: tuple
is_significant: bool
sample_sizes: dict
class ABTestAnalyzer:
"""Statistical A/B test analysis for ML models."""
def __init__(
self,
alpha: float = 0.05,
power: float = 0.8,
min_effect_size: float = 0.02
):
self.alpha = alpha
self.power = power
self.min_effect_size = min_effect_size
def calculate_sample_size(
self,
baseline_rate: float,
expected_lift: float
) -> int:
"""Calculate required sample size per variant."""
from statsmodels.stats.power import TTestIndPower
effect_size = expected_lift / baseline_rate
analysis = TTestIndPower()
sample_size = analysis.solve_power(
effect_size=effect_size,
alpha=self.alpha,
power=self.power,
ratio=1.0,
alternative='two-sided'
)
return int(np.ceil(sample_size))
def analyze_continuous(
self,
control: np.ndarray,
treatment: np.ndarray,
metric_name: str = "conversion"
) -> ABTestResult:
"""
Analyze A/B test with continuous metric (t-test).
"""
# Two-sample t-test
t_stat, p_value = stats.ttest_ind(control, treatment)
control_mean = np.mean(control)
treatment_mean = np.mean(treatment)
lift = (treatment_mean - control_mean) / control_mean
# Confidence interval for difference
pooled_se = np.sqrt(
np.var(control) / len(control) +
np.var(treatment) / len(treatment)
)
ci = stats.t.interval(
1 - self.alpha,
len(control) + len(treatment) - 2,
loc=treatment_mean - control_mean,
scale=pooled_se
)
is_significant = p_value < self.alpha
winner = None
if is_significant:
winner = "treatment" if treatment_mean > control_mean else "control"
return ABTestResult(
winner=winner,
p_value=p_value,
control_mean=control_mean,
treatment_mean=treatment_mean,
lift=lift,
confidence_interval=ci,
is_significant=is_significant,
sample_sizes={"control": len(control), "treatment": len(treatment)}
)
def analyze_proportions(
self,
control_successes: int,
control_total: int,
treatment_successes: int,
treatment_total: int
) -> ABTestResult:
"""
Analyze A/B test with binary metric (chi-square).
"""
# Chi-square test
contingency = np.array([
[control_successes, control_total - control_successes],
[treatment_successes, treatment_total - treatment_successes]
])
chi2, p_value, _, _ = stats.chi2_contingency(contingency)
control_rate = control_successes / control_total
treatment_rate = treatment_successes / treatment_total
lift = (treatment_rate - control_rate) / control_rate
# Wilson confidence interval
ci = self._wilson_ci(
treatment_rate - control_rate,
treatment_total,
self.alpha
)
is_significant = p_value < self.alpha
winner = None
if is_significant:
winner = "treatment" if treatment_rate > control_rate else "control"
return ABTestResult(
winner=winner,
p_value=p_value,
control_mean=control_rate,
treatment_mean=treatment_rate,
lift=lift,
confidence_interval=ci,
is_significant=is_significant,
sample_sizes={"control": control_total, "treatment": treatment_total}
)
def _wilson_ci(self, p: float, n: int, alpha: float) -> tuple:
"""Calculate Wilson score confidence interval."""
z = stats.norm.ppf(1 - alpha / 2)
denominator = 1 + z**2 / n
center = (p + z**2 / (2 * n)) / denominator
spread = z * np.sqrt((p * (1 - p) + z**2 / (4 * n)) / n) / denominator
return (center - spread, center + spread)
START: What type of monitoring?
│
├─→ [Real-time] → Latency constraint?
│ ├─→ <100ms: Statistical tests (PSI, KS)
│ └─→ Flexible: Model-based detection
│
├─→ [Batch] → Data volume?
│ ├─→ Large: Sampling + Evidently
│ └─→ Small: Full comparison
│
└─→ [Continuous] → Use sliding windows
├─→ Page-Hinkley for gradual drift
└─→ ADWIN for sudden drift
┌─────────────────┬───────────┬───────────┬────────────┐
│ Metric │ Warning │ Critical │ Action │
├─────────────────┼───────────┼───────────┼────────────┤
│ Accuracy │ <90% │ <80% │ Retrain │
│ Latency P99 │ >200ms │ >500ms │ Scale/Opt │
│ Error Rate │ >1% │ >5% │ Rollback │
│ Drift Score │ >0.1 │ >0.3 │ Investigate│
│ Throughput │ <80% exp │ <50% exp │ Scale up │
└─────────────────┴───────────┴───────────┴────────────┘
| Issue | Root Cause | Detection | Resolution |
|---|---|---|---|
| False drift alerts | Low sample size | Alert noise | Increase window size |
| Missed drift | Threshold too high | Performance drop | Lower thresholds |
| Alert fatigue | Too many metrics | Team ignores alerts | Prioritize, aggregate |
| Delayed detection | Batch processing lag | Stale metrics | Reduce batch interval |
| Metric gaps | Collection failures | Missing data points | Add redundancy |
□ 1. Verify reference data quality
□ 2. Check sample sizes are sufficient
□ 3. Validate statistical test assumptions
□ 4. Confirm metric collection is working
□ 5. Test alert routing end-to-end
□ 6. Verify drift thresholds are calibrated
□ 7. Check for seasonal patterns
□ 8. Validate ground truth availability
[INFO] monitoring_started → Monitoring pipeline active
[INFO] metrics_collected → Batch metrics collected
[WARN] drift_warning → Drift score approaching threshold
[WARN] sample_size_low → Insufficient data for significance
[ERROR] collection_failed → Failed to collect metrics
[ERROR] alert_send_failed → Alert notification failed
[FATAL] monitoring_down → Monitoring pipeline stopped
ml-monitoring (PRIMARY_BOND)04-training-pipelines - receives training metrics baseline05-model-serving - receives serving metrics04-training-pipelines - triggers retraining01-mlops-fundamentals - informs process improvements| Version | Date | Changes |
|---|---|---|
| 2.0.0 | 2024-12 | Production-grade: Evidently, Prometheus, A/B testing |
| 1.0.0 | 2024-11 | Initial release with SASMP v1.3.0 compliance |
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