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Monitoring & Dashboard

Overview

The Monitoring & Dashboard component provides comprehensive observability and analytics capabilities for the AI Marketplace platform. It enables real-time monitoring, alerting, and visualization of system performance, resource usage, and business metrics.

Key Features

  • 📊 Real-time Monitoring
  • System performance metrics
  • Resource utilization tracking
  • API usage statistics

  • Cost monitoring

  • 🔔 Alerting System

  • Custom alert rules
  • Multi-channel notifications
  • Alert aggregation

  • Incident management

  • 📈 Analytics Dashboard

  • Customizable widgets
  • Interactive visualizations
  • Export capabilities
  • Scheduled reports

Architecture

The monitoring architecture is centered on these main layers:

  1. Data Collectors for telemetry intake
  2. Time Series DB, Log Aggregator, and Metrics Store for storage
  3. Dashboard Engine for analytics and reporting
  4. Alert Manager for threshold-based actions
  5. Visualization Layer for dashboards
  6. Notification Service for outbound alerts

Getting Started

Prerequisites

  • Python 3.8+ for SDK usage
  • Basic understanding of monitoring concepts
  • API key for authentication

Quick Start

  1. Install the SDK: 

    pip install ai-marketplace-monitoring

  2. Initialize the client: 

    from ai_marketplace.monitoring import MonitoringClient

client = MonitoringClient(api_key="your-api-key")

  3. Create a dashboard: 

    dashboard = client.create_dashboard(
    name="System Overview",
    description="Key system metrics"
)

  4. Add widgets: 

    dashboard.add_widget(
    type="line_chart",
    title="API Usage",
    metric="api_calls",
    time_range="24h"
)

Best Practices

  1. Metric Collection
  2. Define clear metrics
  3. Set appropriate collection intervals
  4. Implement proper aggregation

  5. Monitor collection overhead

  6. Alert Configuration

  7. Set meaningful thresholds
  8. Use appropriate alert channels
  9. Implement alert grouping

  10. Regular alert review

  11. Dashboard Design

  12. Organize by functionality
  13. Use appropriate visualizations
  14. Implement proper filtering
  15. Regular updates

Advanced Features

Custom Metrics

# Define custom metric
client.create_metric(
    name="custom_metric",
    type="gauge",
    description="Custom business metric",
    labels=["environment", "service"]
)

# Record metric value
client.record_metric(
    name="custom_metric",
    value=42,
    labels={"environment": "prod", "service": "api"}
)

Alert Rules

# Create alert rule
client.create_alert_rule(
    name="high_error_rate",
    condition="error_rate > 0.05",
    duration="5m",
    severity="critical",
    channels=["email", "slack"]
)

Dashboard API

# Export dashboard
dashboard.export(format="pdf")

# Schedule report
dashboard.schedule_report(
    frequency="daily",
    format="pdf",
    recipients=["[email protected]"]
)

Monitoring and Analytics

  • System health metrics
  • Performance indicators
  • Cost analysis
  • Usage patterns

Support and Resources