Typical model lifecycle without monitoring
18% accuracy loss = €40,000/year lost arbitrage profits for 1 MW BESS
Why do AI models fail in production?
ML models aren't 'fire and forget' systems. The world changes - and your model must adapt.
Model Drift
Your AI loses accuracy daily: New electricity tariffs, weather patterns, regulations - the model was trained on 2023 data, but 2024 looks different. After 6 months, accuracy drops from 96% to 78%.
Black Box Models
Your model outputs predictions - but why? Without explainability and monitoring, you don't know if the model is still working correctly or if it's already making wrong decisions that cost you money.
Live Monitoring Dashboard (Example)
Professional ML Monitoring for Energy
Detect problems in minutes instead of months - and maintain your 96% accuracy permanently.
Full Observability
Transparent insights into every decision your model makes: SHAP values, feature importance, confidence intervals. You see exactly why your model predicted €85/MWh.
Intelligent Alerting
No alert fatigue: Our alerts consider context, trends and severity. You only get notified when action is truly needed - via email, SMS, Slack or Teams.
Auto Retraining
Fully automatic retraining pipeline: When drift score exceeds threshold, model is trained with new data, validated and deployed - without manual intervention.
Return on Investment
ML Monitoring costs €500-2,000/month. What you save:
Complete MLOps Platform
Performance Metrics
Continuous tracking of MAPE, RMSE, R² for each forecast type. Dashboard with historical trends and anomaly detection.
Explainability
SHAP values for every prediction: Why did the model predict €85/MWh? Transparency for audits and stakeholders.
Model Versioning
Complete version control: Who deployed what when? Rollback to any version possible. Git-like history.
A/B Testing
Test new models against production: 10% traffic to challenger model, statistical significance tests.
Data Quality
Automatic validation of all input data: Missing values, outliers, schema changes. Early detection of data pipeline issues.
ML Governance
Complete audit trail for GDPR compliance. Documentation of every prediction, training, and deployment.
Passende Tools auf stromfee.club
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Our AI phone assistant explains ML monitoring, drift detection and retraining strategies - clearly and without buzzwords.
Frequently Asked Questions
Model drift means your ML model loses accuracy over time because input data (data drift) or underlying relationships (concept drift) change. In energy, this happens constantly: new electricity tariffs, changed weather patterns, new regulations. Without monitoring, you only notice drift when results become measurably worse - often months later.
That depends on drift - not a fixed schedule. Some models (e.g., weather forecast) need weekly retraining, others (e.g., asset classification) are stable for years. Our monitoring shows you when retraining makes sense - based on actual drift metrics, not assumptions.
Yes. Our monitoring platform is model-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM and any other framework. We only need access to predictions and ground truth data - the model itself doesn't need to be changed.
Our ML monitoring costs €500-2,000/month, depending on the number of models and predictions. For a typical 1 MW BESS, where 18% accuracy loss costs €40,000/year, the monitoring pays for itself in a few weeks.
Yes, we offer both managed service and self-hosted options. With self-hosted, we deploy the platform in your infrastructure (on-premise or cloud), you operate it yourself. We train your team and provide support.