ML Model Monitoring That Catches Issues Before They Impact Production

Detect drift, bias, and performance degradation in real-time. KOSHERCO provides enterprise-grade monitoring for your production ML models with automated alerting and comprehensive observability.

Real-time drift detection
Automated bias monitoring
Performance tracking
Model Performance Dashboard
Model Accuracy
94.7%
↑ 2.3%
Drift Score
0.12
↓ 0.04
Bias Index
0.08
Normal

Comprehensive ML Model Monitoring

KOSHERCO provides end-to-end monitoring capabilities to ensure your models perform reliably in production environments.

Data Drift Detection

Monitor input data distributions and detect statistical shifts that could impact model performance before they affect your business outcomes.

  • Kolmogorov-Smirnov test for continuous features
  • Chi-squared test for categorical variables
  • Population stability index tracking

Performance Monitoring

Track key performance metrics in real-time with customizable thresholds and automated alerting for degradation patterns.

  • Accuracy, precision, recall, F1 tracking
  • Custom business metrics integration
  • Automated performance reports

Bias Detection

Ensure fairness across different demographic groups with advanced bias detection algorithms and compliance reporting.

  • Demographic parity monitoring
  • Equal opportunity analysis
  • Regulatory compliance reporting

AI-Powered Monitoring Dashboard

Get instant visibility into your model's health with our intelligent monitoring system that learns from your models' behavior patterns.

Prediction Service Alpha

Healthy
Requests/min
1,247
Latency p99
48ms
Error Rate
0.02%
Drift Score
0.11

Recommendation Engine v2

Warning
Requests/min
892
Latency p99
124ms
Error Rate
0.18%
Drift Score
0.34

Recent Alerts

Data drift detected in feature_age
2 hours ago • Recommendation Engine v2
Bias threshold exceeded for gender attribute
5 hours ago • Credit Scoring Model

Model Performance Trends

7-Day Accuracy
93.2%
30-Day Accuracy
94.1%
Prediction Volume
2.3M
Active Models
12

Enterprise-Grade ML Observability

Built for scale, designed for reliability. KOSHERCO handles billions of predictions while maintaining sub-second detection latency.

Compliance & Governance

Meet regulatory requirements with built-in compliance features for GDPR, CCPA, and industry-specific regulations. Automated audit trails and reporting ensure you're always ready for compliance reviews.

Automated Audit Logs
Complete tracking of all model decisions, data access, and system changes with immutable audit trails.
Regulatory Templates
Pre-built compliance templates for GDPR, CCPA, SOC 2, ISO 27001, and industry-specific regulations.
Privacy-Preserving Analytics
Monitor sensitive models without exposing PII using differential privacy and secure aggregation.

Intelligent Alerting

Smart alert routing with context-aware notifications. Our ML-powered alert system reduces false positives by 85% while ensuring critical issues are never missed.

Anomaly Detection
Advanced algorithms detect unusual patterns before they impact performance, with automatic root cause analysis.
Smart Prioritization
ML-driven alert scoring prioritizes issues based on business impact and historical resolution patterns.
Contextual Notifications
Rich alerts include relevant metrics, affected segments, and suggested remediation actions.

Built for Enterprise Workloads

Handle millions of predictions per second with sub-millisecond detection latency. Our distributed architecture ensures reliability at any scale.

99.99%
Uptime SLA
Enterprise-grade reliability with redundant systems
50ms
Detection Latency
Real-time drift detection at production speed
10B+
Daily Predictions
Proven scale across Fortune 500 deployments
85%
Alert Reduction
Fewer false positives with intelligent filtering

Works With Your ML Stack

Native integrations with popular ML frameworks, cloud platforms, and data infrastructure. Deploy monitoring in minutes, not months.

Monitor Any Production Model

From financial risk models to recommendation engines, KOSHERCO adapts to your specific monitoring needs.

Financial Risk Models

Ensure credit scoring, fraud detection, and risk assessment models maintain accuracy and fairness across all customer segments. Detect economic drift and market changes that impact model performance.

  • Credit Scoring Compliance

    Monitor for discriminatory patterns and ensure ECOA/FCRA compliance

  • Fraud Detection Accuracy

    Track false positive rates and detect emerging fraud patterns in real-time

  • Market Risk Monitoring

    Identify when market conditions deviate from training data distributions

# Initialize KOSHERCO monitoring
from kosherco import ModelMonitor

# Configure credit scoring monitor
monitor = ModelMonitor(
  model_id="credit_score_v3",
  environment="production",
  drift_threshold=0.15,
  bias_attributes=["age", "gender", "race"]
)

# Track prediction with monitoring
prediction = model.predict(features)
monitor.log_prediction(
  features=features,
  prediction=prediction,
  metadata={"region": "US-WEST"}
)

Recommendation Engines

Monitor personalization models for bias, relevance degradation, and seasonal drift. Ensure recommendations remain diverse and engaging across all user segments.

  • Engagement Metrics

    Track click-through rates and conversion metrics in real-time

  • Diversity Monitoring

    Ensure recommendations maintain healthy diversity across categories

  • Seasonal Adaptation

    Detect and adapt to seasonal shopping pattern changes automatically

# Monitor recommendation diversity
from kosherco.metrics import DiversityMonitor

diversity = DiversityMonitor(
  model_id="rec_engine_v2",
  min_diversity=0.7,
  categories=["electronics", "fashion", "home"]
)

# Check recommendation quality
recs = model.get_recommendations(user_id)
diversity.evaluate(recs)

if diversity.score < threshold:
  alert.trigger("Low diversity detected")

Clinical Decision Support

Monitor diagnostic and treatment recommendation models for accuracy, bias across patient populations, and compliance with medical standards.

  • Diagnostic Accuracy

    Track sensitivity and specificity across different patient demographics

  • Population Health Equity

    Ensure equal performance across all patient populations

  • Clinical Validation

    Continuous validation against clinical outcomes and guidelines

# Healthcare model monitoring
from kosherco.healthcare import ClinicalMonitor

monitor = ClinicalMonitor(
  model_id="diagnosis_assist_v4",
  compliance_mode="HIPAA",
  protected_attributes=["age", "ethnicity"]
)

# Monitor prediction with outcome
diagnosis = model.predict(patient_data)
monitor.track_prediction(
  prediction=diagnosis,
  confidence=confidence_score,
  outcome_available=False
)

Predictive Maintenance

Monitor equipment failure prediction models for accuracy and detect when sensor patterns indicate new failure modes or environmental changes.

  • Failure Prediction

    Track prediction accuracy and lead time for maintenance events

  • Sensor Drift Detection

    Identify when sensor readings deviate from expected patterns

  • Cost Optimization

    Balance false positives against unplanned downtime costs

# IoT sensor monitoring
from kosherco.iot import SensorMonitor

sensor_monitor = SensorMonitor(
  model_id="turbine_failure_v2",
  sensor_channels=32,
  drift_window="7d"
)

# Real-time sensor analysis
sensor_data = turbine.get_sensor_data()
risk_score = model.predict_failure(sensor_data)
sensor_monitor.analyze(sensor_data, risk_score)

Start Monitoring Your Models Today

Join leading enterprises using KOSHERCO to ensure their ML models perform reliably in production. Get started with a comprehensive demo and see the difference real monitoring makes.

Ready to Monitor Your Models?

Our team of ML monitoring experts is ready to help you implement comprehensive observability for your production models.

Our team typically responds within 2 business hours. For immediate assistance, call us at 805-489-2180