Mission control
Configure the production environment, monitoring window, and game rules.
Alert thresholds
Tune monitoring sensitivity.
Accessibility
Live model health
Start the simulation to begin monitoring.
Idle
Accuracy
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No data
Precision
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No data
Recall
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No data
F1 score
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No data
Error rate
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No data
Drift score
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No data
Latency
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No data
Confidence
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No data
Version: v3.2 production
Throughput: —
Active alerts: 0
Review queue: 0
Specificity: —
FPR: —
FNR: —
Log loss: —
MAE: —
MSE: —
RMSE: —
R²: —
Production stream canvas
Animated requests show predictions, errors, drift, alerts, and infrastructure pressure.
Incoming
Correct
Error
Drift
Ingest
Preprocess
Predict
Evaluate
Respond
Performance analytics
Plotly charts update from the selected horizon and monitoring window.
Alerts and investigations
Inspect breached thresholds and likely causes.
No alerts yet.
Select a cause and investigate after an incident appears.
Mission score
Total score
0
Monitoring Beginner
Performance
0
Detection
0
Decisions
0
Reliability
0
Business cost$0
Avoided failures0
Missed warnings0
Corrective actions
Every response changes cost, risk, downtime, and future performance.
Human review queue
Low-confidence and high-risk predictions await review.
The queue is empty.
Review accuracy: 96%
Cost/item: $2
Delay: 0 min
Event log
Metrics, incidents, deployments, decisions, and recoveries.
No events recorded.
Custom monitoring data
Paste labels, predictions, confidence, latency, version, and timestamp records.
Monitoring knowledge challenge
Answer questions about metrics, thresholds, drift, rollback, and retraining.
Start the simulation or load a quiz question.
Incident summary and learning guide
Review formulas, interpretation, limitations, and recommended responses.
Current round summary
No round data is available yet.
Recommended response
Begin monitoring to receive a recommendation.
Core formulas
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 = 2 × Precision × Recall / (Precision + Recall)
Specificity = TN / (TN + FP)
FPR = FP / (FP + TN)
FNR = FN / (FN + TP)
MAE = mean(|actual − predicted|)
RMSE = √mean((actual − predicted)²)
| Metric | What it measures | Useful when | Common limitation |
|---|---|---|---|
| Accuracy | Overall correct predictions | Classes are balanced | Can hide minority-class failure |
| Precision | Reliability of positive predictions | False positives are costly | Ignores missed positives |
| Recall | Coverage of actual positives | False negatives are costly | May increase false positives |
| F1 score | Balance between precision and recall | Both error types matter | Hides true negatives |
| Log loss | Probability calibration quality | Confidence matters | Highly penalises confident errors |
| Drift score | Input distribution change | Production data evolves | Does not prove performance loss |
| Latency | Prediction response time | Real-time systems | Does not measure accuracy |
Export and reporting
Download monitoring data, incidents, summaries, or print the complete report.