Analytics
Monitor and analyze your ML operations with comprehensive analytics and insights.
Overview
The Analytics Dashboard provides comprehensive insights and metrics for the Deep Learning Platform, helping you track performance, costs, and usage across all your ML operations.
Dashboard Summary Cards
At the top of the dashboard, you'll see key metrics at a glance:
Active Experiments
Count: Number of currently running ML experiments
Trend: Percentage change from previous period
Total Users
Count: Total number of platform users
Trend: Percentage change from previous period
Deployed Models
Count: Number of models in production
Trend: Percentage change from previous period
Total Projects
Count: Number of active projects
Trend: Percentage change from previous period
GPU Utilization
Percentage: Current GPU usage percentage
Status: Infrastructure cost with trend indicators
Total Cost Total Cost
Amount: Total infrastructure cost
Trend: Percentage change from previous period
Detailed Analytics
The dashboard provides detailed analytics with customizable time ranges (Last 24 Hours, Last 7 Days, Last 30 Days, etc.).
Available Actions:
Refresh: Update data
Print: Print dashboard
Export: Export analytics data
System Performance
Real-time monitoring of system resources:
CPU Usage
Current utilization: 83.4%
Visual progress bar indicator
Color-coded (cyan)
Memory Usage
Current utilization: 71.2%
Visual progress bar indicator
Color-coded (purple)
GPU Usage
Current utilization: 89.1%
Visual progress bar indicator
Color-coded (green)
Disk Usage
Current utilization: 45.0%
Visual progress bar indicator
Color-coded (lime green)
Real-time CPU Usage
Interactive line chart showing CPU usage over time:
Time-series visualization
Real-time updates
Hover for detailed values
CPU % on Y-axis
Cost Analytics
Cost Analytics Chart:
Area chart showing cost trends over time
Time-series data (hourly/daily)
Cost ($) on Y-axis
Visual trend analysis
Cost by Service (Pie Chart):
Data Transfer: Largest portion (blue)
GPU Compute: Second largest (purple/pink)
Load Balancing: Smaller portion (cyan)
Storage: Smallest portion (pink)
Interactive legend
Model Performance Metrics
Line chart tracking model performance:
Accuracy: Green line
Precision: Cyan line
Recall: Purple line
Time-series visualization
Performance trends over time
Cost by Environment
Pie chart showing cost distribution:
Development: Largest portion (blue)
Production: Medium portion (purple/pink)
Staging: Smallest portion (pink)
Interactive breakdown
Key Features
Experiment Analytics
Training metrics over time
Hyperparameter impact analysis
Experiment comparison dashboards
Success/failure rates
Active experiment tracking
Model Performance
Model accuracy trends
Inference latency tracking
Model drift detection
Performance metrics visualization
Multi-metric comparison
Resource Usage
GPU/CPU utilization monitoring
Memory consumption tracking
Storage usage analysis
Real-time performance graphs
Resource optimization insights
Deployment Metrics
Request rate and throughput
Error rates and types
Latency percentiles (p50, p95, p99)
Uptime and availability
Deployment health status
Cost Analysis
Total infrastructure cost tracking
Cost breakdown by service
Cost by environment
Trend analysis
Budget monitoring
Using the Dashboard
Time Range Selection:
Click the time range dropdown (default: "Last 24 Hours")
Select desired range:
Last 24 Hours
Last 7 Days
Last 30 Days
Custom range
Refreshing Data:
Click Refresh button to update all metrics
Dashboard auto-refreshes periodically
Exporting Data:
Click Export button
Choose export format
Download analytics report
Printing:
Click Print button to print current dashboard view
Monitoring Best Practices
Regular Monitoring:
Check dashboard daily for anomalies
Monitor GPU utilization for optimization
Track cost trends to manage budget
Review model performance metrics
Setting Baselines:
Establish normal ranges for metrics
Set up alerts for deviations
Track trends over time
Cost Optimization:
Identify high-cost services
Optimize resource allocation
Monitor environment-specific costs
Review and adjust as needed
Next Steps
View detailed Experiments metrics
Monitor Deployments performance
Track Models accuracy
Optimize resource usage based on insights
Last updated