mazdek
AI Governance • Technology

AI Model Monitor

Monitors AI models in production for drift, bias, and performance degradation. The agent detects problems early and automatically alerts.

90% faster problem detection
MLOps Model Drift Bias Detection Performance Monitoring

90%

Faster Problem Detection

24/7

Continuous Monitoring

< 5min

Alert Latency

50+

Monitored Metrics

About This Solution

How Does the AI Model Monitor Work?

The AI Model Monitor is your guardian for AI systems in production. After a model is deployed, the invisible erosion of quality often begins — data drift, concept drift, or creeping bias problems.

Our agent continuously analyzes the input data and predictions of your models. It detects when the data distribution changes, when model performance degrades, or when certain groups are systematically disadvantaged.

Through statistical tests and machine learning, the agent identifies problems often weeks before they become visible to humans. Automatic alerts and detailed diagnostics enable quick action.

Features

What This Agent Can Do

Data Drift Detection

Detects changes in input data distribution with statistical tests like PSI, KS test, and Wasserstein distance.

Bias Monitoring

Continuous fairness metrics for protected attributes like gender, age, and origin.

Performance Tracking

Real-time metrics for accuracy, precision, recall, F1, and business-specific KPIs.

Automatic Alerts

Intelligent notifications based on thresholds, trends, and anomalies.

Examples

How It Works in Practice

1

Credit Risk Model

"A scoring model suddenly shows higher rejection rates for a specific age group."

Agent detects the bias drift within hours, alerts the team, and provides root cause analysis.

2

Fraud Detection

"Fraudsters change their behavior patterns, the feature distribution in production traffic deviates from training."

Data drift is detected before the false-negative rate becomes critical. Retraining is recommended.

3

Recommendation System

"After an assortment change, the recommendation model performs worse for new product categories."

Performance degradation is analyzed by segment, targeted fine-tuning is suggested.

FAQ

Frequently Asked Questions

Which ML frameworks are supported?
We support all common frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, as well as proprietary models via a standardized API.
How are ground-truth labels handled?
The monitor also works without immediate labels (unsupervised). Once labels become available (delayed), performance metrics are automatically updated.
Can I define custom metrics?
Yes, in addition to standard ML metrics, you can define business-specific KPIs that are included in monitoring.
How does it integrate with existing MLOps pipelines?
Native integration with MLflow, Kubeflow, SageMaker, Vertex AI, and others. Webhook-based integration for custom pipelines is also possible.

Interested in This Solution?

Let's discuss together how the AI Model Monitor can monitor your AI systems.

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