MonitorX
ML/AI Infrastructure Observability Platform
Interactive Demo
Experience real-time ML monitoring with live metrics, drift detection, and intelligent alerts. This interactive demo showcases MonitorX capabilities in production environments.
Dashboard Controls
Simulation Stopped
Real-Time ML Observability Dashboard
Comprehensive monitoring across your entire ML infrastructure
Live Model Performance
Live Updates
GPT-4 Inference
Healthy Avg Latency 245ms
Throughput 1.2K req/min
Error Rate 0.02%
Cost/1K req $0.12
Image Classification
Warning Avg Latency 89ms
Throughput 850 req/min
GPU Usage 87%
Accuracy 96.4%
Fraud Detection
Healthy Avg Latency 12ms
Throughput 5.2K req/min
Accuracy 98.7%
Precision 97.8%
Latency Trends (Last Hour)
60m ago 30m ago Now
Throughput Trends (Last Hour)
60m ago 30m ago Now
Resource Utilization
CPU Usage
45%Memory Usage
68%GPU Usage
87%Network I/O
32%Model Drift Detection
Feature Drift Analysis
Feature A (Age) 0.15
Feature B (Income) 0.65
Feature C (Location) 0.85
Prediction Drift
Drift threshold: 0.7
Model Comparison & A/B Testing
Champion Model (Current)
Champion Accuracy 96.4%
Latency 89ms
Cost/1K req $0.08
Traffic Split 70%
Challenger Model (Testing)
Challenger Accuracy 97.1%
Latency 125ms
Cost/1K req $0.12
Traffic Split 30%
A/B Test Results
Challenger shows 0.7% accuracy improvement but 40% higher latency
Intelligent Alert Center
Critical: Model Performance Degradation
1 min ago
Image Classification accuracy dropped to 89.2% - below 95% threshold
High GPU Utilization
2 min ago
Image Classification model GPU usage at 87% - consider scaling
Model Drift Detected - Resolved
15 min ago
Fraud detection model retrained successfully - accuracy restored to 98.7%
Cost Optimization Opportunity
30 min ago
Switching to smaller instance could save 23% on GPT-4 inference costs