Documentation | Augment AI Platform

Installation

Deploy AI MonitorIQ to automatically set up intelligent monitoring and predictive alerting for your infrastructure.

System Requirements

Python 3.9 or higher
Prometheus 2.30+ or compatible metrics system
Grafana 8.0+ (for dashboard visualization)
Minimum 4GB RAM (8GB recommended for large environments)
Network access to monitoring targets and notification systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-monitor-iq

# Install via Docker
docker pull augment/monitor-iq:latest

# Install from source
git clone https://github.com/augment-ai/monitor-iq
cd monitor-iq
pip install -e .

# Install monitoring dependencies
pip install prometheus-client grafana-api pyyaml

# Verify installation
monitor-iq --version

Monitoring Stack Integration

Configure integration with your monitoring infrastructure:

Stack Integrationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure Prometheus integration
export PROMETHEUS_URL=http://prometheus:9090
export PROMETHEUS_USER=admin
export PROMETHEUS_PASS=your_password

# Configure Grafana integration
export GRAFANA_URL=http://grafana:3000
export GRAFANA_TOKEN=your_grafana_token

# Configure notification channels
export SLACK_WEBHOOK=https://hooks.slack.com/...
export PAGERDUTY_TOKEN=your_pagerduty_token

# Initialize monitor IQ
monitor-iq init --discover-infrastructure

# Verify integrations
monitor-iq health-check

Quick Start

Get intelligent monitoring and predictive alerts running in minutes with automated configuration.

1. Discover Infrastructure

Infrastructure Discoverybash

# Auto-discover infrastructure components
monitor-iq discover --providers aws,azure,kubernetes
monitor-iq discover --services web,database,cache,queue

# Analyze current monitoring coverage
monitor-iq coverage --analyze-gaps --recommend-additions

# Generate monitoring plan
monitor-iq plan --priority critical,high --include-predictions

2. Deploy Monitoring

Monitoring Deploymentbash

# Deploy intelligent monitoring configuration
monitor-iq deploy --auto-configure --enable-predictions

# Set up predictive alerts
monitor-iq alerts create --type predictive --confidence 0.8
monitor-iq alerts create --type anomaly --sensitivity medium

# Configure notification channels
monitor-iq notifications add slack --channel "#alerts" --urgency high
monitor-iq notifications add email --recipients "ops@company.com"

3. Monitor and Optimize

Monitoring Operationsbash

# Start intelligent monitoring
monitor-iq start --daemon --predictive-mode

# Monitor system health in real-time
monitor-iq status --live --show-predictions

# Generate monitoring report
monitor-iq report --type health-insights --output monitoring-report.html

# Optimize monitoring configuration
monitor-iq optimize --reduce-noise --improve-accuracy

Configuration

Configure AI MonitorIQ to align with your infrastructure and monitoring requirements.

Basic Configuration

.monitor-iq.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

infrastructure:
  discovery:
    providers: ["aws", "azure", "kubernetes"]
    auto_discover: true
    scan_interval: "1h"
  services:
    - name: "web-tier"
      type: "web"
      endpoints: ["http://app.company.com"]
    - name: "database"
      type: "database"
      hosts: ["db1.company.com", "db2.company.com"]
    - name: "cache"
      type: "redis"
      cluster: "redis.company.com:6379"

monitoring_stack:
  prometheus:
    url: "http://prometheus:9090"
    retention: "30d"
    scrape_interval: "30s"
  grafana:
    url: "http://grafana:3000"
    api_token: "{GRAFANA_TOKEN}"
    create_dashboards: true
  alertmanager:
    url: "http://alertmanager:9093"
    config_path: "/etc/alertmanager/"

ai_configuration:
  predictive_alerts:
    enabled: true
    prediction_window: "2h"
    confidence_threshold: 0.8
    models: ["anomaly", "trend", "seasonal"]
  smart_thresholds:
    enabled: true
    baseline_period: "7d"
    adaptation_rate: "daily"
  noise_reduction:
    enabled: true
    correlation_window: "5m"
    suppress_duplicates: true

alert_rules:
  system_health:
    cpu_usage:
      warning: 70
      critical: 85
      prediction: true
    memory_usage:
      warning: 75
      critical: 90
      prediction: true
    disk_usage:
      warning: 80
      critical: 95
      prediction: true
  application_health:
    response_time:
      warning: "500ms"
      critical: "2s"
      prediction: true
    error_rate:
      warning: 5
      critical: 10
      prediction: true

notifications:
  channels:
    - name: "slack-critical"
      type: "slack"
      webhook: "{SLACK_WEBHOOK}"
      urgency: ["critical"]
    - name: "email-team"
      type: "email"
      recipients: ["ops@company.com"]
      urgency: ["warning", "critical"]
    - name: "pagerduty"
      type: "pagerduty"
      service_key: "{PAGERDUTY_KEY}"
      urgency: ["critical"]

automation:
  auto_remediation:
    enabled: true
    safe_actions_only: true
    approval_required: ["restart_service", "scale_resources"]
  monitoring_optimization:
    enabled: true
    optimization_interval: "24h"
    learn_from_feedback: true

Predictive Alerts

AI MonitorIQ uses advanced machine learning to predict issues before they impact your systems.

Anomaly Prediction

• Unusual pattern detection
• Performance degradation prediction
• Resource exhaustion forecasting
• Service disruption early warning

Trend Analysis

• Growth trend monitoring
• Capacity planning insights
• Performance trend analysis
• Usage pattern evolution

Seasonal Patterns

• Daily usage cycle analysis
• Weekly pattern recognition
• Monthly seasonal adjustments
• Holiday traffic predictions

Correlation Analysis

• Cross-service impact analysis
• Dependency chain monitoring
• Cascading failure prediction
• Root cause correlation

Environment Variables

Configure AI MonitorIQ behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
MONITOR_IQ_CONFIG	Path to configuration file	.monitor-iq.yaml
PROMETHEUS_URL	Prometheus server URL	http://localhost:9090
MONITOR_IQ_LOG_LEVEL	Logging level (debug/info/warn/error)	info

Basic Usage

Learn the fundamental monitoring intelligence patterns and observability workflows.

Monitoring Commands

Monitoring Intelligencebash

# Deploy intelligent monitoring for discovered services
monitor-iq deploy --services all --enable-predictions

# Create smart alerts with AI-optimized thresholds
monitor-iq alerts create --smart-thresholds --learn-from-history

# Monitor system health with predictive insights
monitor-iq monitor --predictive --real-time --dashboard

# Optimize existing monitoring configuration
monitor-iq optimize --reduce-alerts --improve-coverage

CLI Commands Reference

Complete reference for all monitoring intelligence and predictive alerting commands.

deploy

Deploy intelligent monitoring configuration with AI-optimized settings

monitor-iq deploy [options]

Options:
  --services <services>    Services to monitor (all|web|database|cache)
  --auto-configure         Automatically configure optimal monitoring
  --enable-predictions     Enable predictive alerting
  --monitoring-stack <stack> Target stack (prometheus|datadog|newrelic)
  --template <template>    Use monitoring template
  --dry-run                Preview configuration without deploying
  --update-existing        Update existing monitoring configuration
  --backup-config          Backup current configuration before changes

predict

Configure and manage predictive monitoring capabilities

monitor-iq predict [options]

Options:
  --enable <models>        Enable prediction models (anomaly|trend|seasonal)
  --confidence <level>     Minimum prediction confidence threshold
  --window <duration>      Prediction time window (1h|2h|4h|8h)
  --train                  Train prediction models on historical data
  --validate               Validate prediction model accuracy
  --tune                   Tune model parameters for better accuracy
  --forecast <metric>      Generate forecast for specific metric

Best Practices

Monitoring intelligence best practices to maximize observability while minimizing alert fatigue.

Intelligent Monitoring Strategy

Start with auto-discovery to understand infrastructure scope
Implement predictive alerts gradually to build confidence
Use smart thresholds to reduce false positive alerts
Monitor prediction accuracy and tune models regularly
Integrate with existing incident response workflows
Continuously optimize monitoring based on feedback

Monitoring Templates

Pre-built monitoring templates for common infrastructure patterns and application architectures.

Available Templates

Web Application Stack

Complete monitoring for web application infrastructure

monitor-iq deploy --template web-stack \
  --components "load-balancer,web-servers,database" \
  --auto-scaling-groups "web-asg"

Microservices Platform

Service mesh and container monitoring

monitor-iq deploy --template microservices \
  --kubernetes-cluster "prod-cluster" \
  --service-mesh "istio"

Data Pipeline

ETL and data processing pipeline monitoring

monitor-iq deploy --template data-pipeline \
  --components "kafka,spark,airflow" \
  --data-stores "s3,redshift"

Serverless Functions

Lambda and serverless function monitoring

monitor-iq deploy --template serverless \
  --functions "api-functions,data-processors" \
  --api-gateways "api-v1,api-v2"

Health Insights

AI-powered health insights provide deep analysis of system performance and reliability trends.

Insight Categories

Performance Insights

Analysis of performance trends and optimization opportunities

Reliability Insights

System reliability metrics and failure pattern analysis

Efficiency Insights

Resource utilization and cost optimization recommendations

Generate Health Insights

Health Analysisbash

# Generate comprehensive health report
monitor-iq insights --type health --timeframe 30d --include-predictions

# Analyze specific service health
monitor-iq insights --service web-tier --focus performance,reliability

# Generate capacity planning insights
monitor-iq insights --type capacity --forecast 90d --growth-analysis

# Create executive health summary
monitor-iq insights --executive-summary --sla-analysis --cost-impact

API Integration

Integrate AI MonitorIQ into your observability stack and incident management workflows.

REST API

API Usagebash

# Deploy monitoring configuration via API
curl -X POST https://api.augment.cfd/v1/monitoring/deploy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "services": ["web", "database"],
    "enable_predictions": true,
    "auto_configure": true,
    "notification_channels": ["slack", "email"]
  }'

Python SDK

Python Integrationpython

from augment_monitor_iq import MonitorIQ

# Initialize monitor IQ
monitor = MonitorIQ(api_key=os.environ['AUGMENT_API_KEY'])

# Deploy intelligent monitoring
deployment = await monitor.deploy_monitoring(
    services=['web', 'database', 'cache'],
    enable_predictions=True,
    auto_configure=True
)

# Get health insights
insights = await monitor.get_health_insights(
    timeframe='7d',
    include_predictions=True,
    focus=['performance', 'reliability']
)

print(f"Generated {len(insights)} health insights")

# Configure predictive alerts
alert_config = await monitor.configure_predictive_alerts(
    models=['anomaly', 'trend'],
    confidence_threshold=0.8,
    prediction_window='2h'
)

# Get monitoring status
status = await monitor.get_monitoring_status(
    include_predictions=True,
    include_health_score=True
)

API Reference

Complete API documentation for integrating monitoring intelligence into your applications.

Monitoring Deployment Endpoint

POST /v1/monitoring/deploy

Deploy intelligent monitoring configuration with AI-optimized settings.

Request Body:

{
  "infrastructure": {
    "providers": ["aws", "kubernetes"],
    "services": ["web", "database", "cache"],
    "auto_discover": true
  },
  "monitoring_config": {
    "enable_predictions": true,
    "prediction_models": ["anomaly", "trend", "seasonal"],
    "confidence_threshold": 0.8,
    "smart_thresholds": true
  },
  "alert_configuration": {
    "notification_channels": [
      {
        "type": "slack",
        "webhook": "https://hooks.slack.com/...",
        "urgency": ["critical", "warning"]
      },
      {
        "type": "email",
        "recipients": ["ops@company.com"],
        "urgency": ["critical"]
      }
    ],
    "escalation_policy": {
      "enabled": true,
      "escalation_time": "15m"
    }
  },
  "optimization_goals": {
    "reduce_alert_noise": true,
    "improve_coverage": true,
    "enable_auto_remediation": false
  }
}

Response:

{
  "deployment_id": "mon-deploy-123",
  "status": "completed",
  "summary": {
    "services_monitored": 8,
    "alerts_configured": 34,
    "predictive_alerts": 12,
    "dashboards_created": 5
  },
  "monitoring_configuration": {
    "prometheus_rules": 28,
    "grafana_dashboards": [
      "infrastructure-overview",
      "application-performance",
      "predictive-insights"
    ],
    "alert_rules": [
      {
        "name": "High CPU Usage Prediction",
        "type": "predictive",
        "service": "web-tier",
        "prediction_window": "2h",
        "confidence": 0.85
      }
    ]
  },
  "health_baseline": {
    "overall_score": 87,
    "performance_score": 92,
    "reliability_score": 84,
    "efficiency_score": 85
  },
  "insights": [
    {
      "type": "optimization",
      "title": "Memory utilization trending upward",
      "service": "web-tier",
      "prediction": "Memory exhaustion predicted in 6 hours",
      "recommendation": "Consider scaling up memory allocation",
      "confidence": 0.89
    }
  ]
}

Troubleshooting

Common issues and solutions when implementing intelligent monitoring and predictive alerting.

Common Issues

High False Positive Rate

Predictive alerts generating too many false positives

Solutions:

Increase confidence threshold for predictive alerts
Extend baseline learning period for better accuracy
Enable smart thresholds to adapt to normal variations
Fine-tune prediction models with recent data

Missing Monitoring Coverage

Auto-discovery not finding all infrastructure components

Solutions:

Verify cloud provider credentials and permissions
Check network connectivity to target systems
Manually add missing services to configuration
Update discovery patterns to include new resources

Performance Impact

Monitoring intelligence causing performance overhead

Solutions:

Adjust monitoring frequency for less critical services
Use sampling for high-volume metrics collection
Optimize prediction model execution schedules
Scale monitoring infrastructure resources

Monitoring Intelligence Documentation Complete!

You now have comprehensive knowledge to implement AI MonitorIQ in your observability stack. From intelligent monitoring setup to predictive alerting, you're equipped to enhance system reliability with AI-powered monitoring intelligence.

Ready to revolutionize your monitoring? Start your free intelligence assessment today and discover how AI can predict issues before they impact your users.

Installation

Deploy AI MonitorIQ to automatically set up intelligent monitoring and predictive alerting for your infrastructure.

System Requirements

Python 3.9 or higher
Prometheus 2.30+ or compatible metrics system
Grafana 8.0+ (for dashboard visualization)
Minimum 4GB RAM (8GB recommended for large environments)
Network access to monitoring targets and notification systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-monitor-iq

# Install via Docker
docker pull augment/monitor-iq:latest

# Install from source
git clone https://github.com/augment-ai/monitor-iq
cd monitor-iq
pip install -e .

# Install monitoring dependencies
pip install prometheus-client grafana-api pyyaml

# Verify installation
monitor-iq --version

Monitoring Stack Integration

Configure integration with your monitoring infrastructure:

Stack Integrationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure Prometheus integration
export PROMETHEUS_URL=http://prometheus:9090
export PROMETHEUS_USER=admin
export PROMETHEUS_PASS=your_password

# Configure Grafana integration
export GRAFANA_URL=http://grafana:3000
export GRAFANA_TOKEN=your_grafana_token

# Configure notification channels
export SLACK_WEBHOOK=https://hooks.slack.com/...
export PAGERDUTY_TOKEN=your_pagerduty_token

# Initialize monitor IQ
monitor-iq init --discover-infrastructure

# Verify integrations
monitor-iq health-check

Quick Start

Get intelligent monitoring and predictive alerts running in minutes with automated configuration.

1. Discover Infrastructure

Infrastructure Discoverybash

# Auto-discover infrastructure components
monitor-iq discover --providers aws,azure,kubernetes
monitor-iq discover --services web,database,cache,queue

# Analyze current monitoring coverage
monitor-iq coverage --analyze-gaps --recommend-additions

# Generate monitoring plan
monitor-iq plan --priority critical,high --include-predictions

2. Deploy Monitoring

Monitoring Deploymentbash

# Deploy intelligent monitoring configuration
monitor-iq deploy --auto-configure --enable-predictions

# Set up predictive alerts
monitor-iq alerts create --type predictive --confidence 0.8
monitor-iq alerts create --type anomaly --sensitivity medium

# Configure notification channels
monitor-iq notifications add slack --channel "#alerts" --urgency high
monitor-iq notifications add email --recipients "ops@company.com"

3. Monitor and Optimize

Monitoring Operationsbash

# Start intelligent monitoring
monitor-iq start --daemon --predictive-mode

# Monitor system health in real-time
monitor-iq status --live --show-predictions

# Generate monitoring report
monitor-iq report --type health-insights --output monitoring-report.html

# Optimize monitoring configuration
monitor-iq optimize --reduce-noise --improve-accuracy

Configuration

Configure AI MonitorIQ to align with your infrastructure and monitoring requirements.

Basic Configuration

.monitor-iq.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

infrastructure:
  discovery:
    providers: ["aws", "azure", "kubernetes"]
    auto_discover: true
    scan_interval: "1h"
  services:
    - name: "web-tier"
      type: "web"
      endpoints: ["http://app.company.com"]
    - name: "database"
      type: "database"
      hosts: ["db1.company.com", "db2.company.com"]
    - name: "cache"
      type: "redis"
      cluster: "redis.company.com:6379"

monitoring_stack:
  prometheus:
    url: "http://prometheus:9090"
    retention: "30d"
    scrape_interval: "30s"
  grafana:
    url: "http://grafana:3000"
    api_token: "{GRAFANA_TOKEN}"
    create_dashboards: true
  alertmanager:
    url: "http://alertmanager:9093"
    config_path: "/etc/alertmanager/"

ai_configuration:
  predictive_alerts:
    enabled: true
    prediction_window: "2h"
    confidence_threshold: 0.8
    models: ["anomaly", "trend", "seasonal"]
  smart_thresholds:
    enabled: true
    baseline_period: "7d"
    adaptation_rate: "daily"
  noise_reduction:
    enabled: true
    correlation_window: "5m"
    suppress_duplicates: true

alert_rules:
  system_health:
    cpu_usage:
      warning: 70
      critical: 85
      prediction: true
    memory_usage:
      warning: 75
      critical: 90
      prediction: true
    disk_usage:
      warning: 80
      critical: 95
      prediction: true
  application_health:
    response_time:
      warning: "500ms"
      critical: "2s"
      prediction: true
    error_rate:
      warning: 5
      critical: 10
      prediction: true

notifications:
  channels:
    - name: "slack-critical"
      type: "slack"
      webhook: "{SLACK_WEBHOOK}"
      urgency: ["critical"]
    - name: "email-team"
      type: "email"
      recipients: ["ops@company.com"]
      urgency: ["warning", "critical"]
    - name: "pagerduty"
      type: "pagerduty"
      service_key: "{PAGERDUTY_KEY}"
      urgency: ["critical"]

automation:
  auto_remediation:
    enabled: true
    safe_actions_only: true
    approval_required: ["restart_service", "scale_resources"]
  monitoring_optimization:
    enabled: true
    optimization_interval: "24h"
    learn_from_feedback: true

Predictive Alerts

AI MonitorIQ uses advanced machine learning to predict issues before they impact your systems.

Anomaly Prediction

• Unusual pattern detection
• Performance degradation prediction
• Resource exhaustion forecasting
• Service disruption early warning

Trend Analysis

• Growth trend monitoring
• Capacity planning insights
• Performance trend analysis
• Usage pattern evolution

Seasonal Patterns

• Daily usage cycle analysis
• Weekly pattern recognition
• Monthly seasonal adjustments
• Holiday traffic predictions

Correlation Analysis

• Cross-service impact analysis
• Dependency chain monitoring
• Cascading failure prediction
• Root cause correlation

Environment Variables

Configure AI MonitorIQ behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
MONITOR_IQ_CONFIG	Path to configuration file	.monitor-iq.yaml
PROMETHEUS_URL	Prometheus server URL	http://localhost:9090
MONITOR_IQ_LOG_LEVEL	Logging level (debug/info/warn/error)	info

Basic Usage

Learn the fundamental monitoring intelligence patterns and observability workflows.

Monitoring Commands

Monitoring Intelligencebash

# Deploy intelligent monitoring for discovered services
monitor-iq deploy --services all --enable-predictions

# Create smart alerts with AI-optimized thresholds
monitor-iq alerts create --smart-thresholds --learn-from-history

# Monitor system health with predictive insights
monitor-iq monitor --predictive --real-time --dashboard

# Optimize existing monitoring configuration
monitor-iq optimize --reduce-alerts --improve-coverage

CLI Commands Reference

Complete reference for all monitoring intelligence and predictive alerting commands.

deploy

Deploy intelligent monitoring configuration with AI-optimized settings

monitor-iq deploy [options]

Options:
  --services <services>    Services to monitor (all|web|database|cache)
  --auto-configure         Automatically configure optimal monitoring
  --enable-predictions     Enable predictive alerting
  --monitoring-stack <stack> Target stack (prometheus|datadog|newrelic)
  --template <template>    Use monitoring template
  --dry-run                Preview configuration without deploying
  --update-existing        Update existing monitoring configuration
  --backup-config          Backup current configuration before changes

predict

Configure and manage predictive monitoring capabilities

monitor-iq predict [options]

Options:
  --enable <models>        Enable prediction models (anomaly|trend|seasonal)
  --confidence <level>     Minimum prediction confidence threshold
  --window <duration>      Prediction time window (1h|2h|4h|8h)
  --train                  Train prediction models on historical data
  --validate               Validate prediction model accuracy
  --tune                   Tune model parameters for better accuracy
  --forecast <metric>      Generate forecast for specific metric

Best Practices

Monitoring intelligence best practices to maximize observability while minimizing alert fatigue.

Intelligent Monitoring Strategy

Start with auto-discovery to understand infrastructure scope
Implement predictive alerts gradually to build confidence
Use smart thresholds to reduce false positive alerts
Monitor prediction accuracy and tune models regularly
Integrate with existing incident response workflows
Continuously optimize monitoring based on feedback

Monitoring Templates

Pre-built monitoring templates for common infrastructure patterns and application architectures.

Available Templates

Web Application Stack

Complete monitoring for web application infrastructure

monitor-iq deploy --template web-stack \
  --components "load-balancer,web-servers,database" \
  --auto-scaling-groups "web-asg"

Microservices Platform

Service mesh and container monitoring

monitor-iq deploy --template microservices \
  --kubernetes-cluster "prod-cluster" \
  --service-mesh "istio"

Data Pipeline

ETL and data processing pipeline monitoring

monitor-iq deploy --template data-pipeline \
  --components "kafka,spark,airflow" \
  --data-stores "s3,redshift"

Serverless Functions

Lambda and serverless function monitoring

monitor-iq deploy --template serverless \
  --functions "api-functions,data-processors" \
  --api-gateways "api-v1,api-v2"

Health Insights

AI-powered health insights provide deep analysis of system performance and reliability trends.

Insight Categories

Performance Insights

Analysis of performance trends and optimization opportunities

Reliability Insights

System reliability metrics and failure pattern analysis

Efficiency Insights

Resource utilization and cost optimization recommendations

Generate Health Insights

Health Analysisbash

# Generate comprehensive health report
monitor-iq insights --type health --timeframe 30d --include-predictions

# Analyze specific service health
monitor-iq insights --service web-tier --focus performance,reliability

# Generate capacity planning insights
monitor-iq insights --type capacity --forecast 90d --growth-analysis

# Create executive health summary
monitor-iq insights --executive-summary --sla-analysis --cost-impact

API Integration

Integrate AI MonitorIQ into your observability stack and incident management workflows.

REST API

API Usagebash

# Deploy monitoring configuration via API
curl -X POST https://api.augment.cfd/v1/monitoring/deploy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "services": ["web", "database"],
    "enable_predictions": true,
    "auto_configure": true,
    "notification_channels": ["slack", "email"]
  }'

Python SDK

Python Integrationpython

from augment_monitor_iq import MonitorIQ

# Initialize monitor IQ
monitor = MonitorIQ(api_key=os.environ['AUGMENT_API_KEY'])

# Deploy intelligent monitoring
deployment = await monitor.deploy_monitoring(
    services=['web', 'database', 'cache'],
    enable_predictions=True,
    auto_configure=True
)

# Get health insights
insights = await monitor.get_health_insights(
    timeframe='7d',
    include_predictions=True,
    focus=['performance', 'reliability']
)

print(f"Generated {len(insights)} health insights")

# Configure predictive alerts
alert_config = await monitor.configure_predictive_alerts(
    models=['anomaly', 'trend'],
    confidence_threshold=0.8,
    prediction_window='2h'
)

# Get monitoring status
status = await monitor.get_monitoring_status(
    include_predictions=True,
    include_health_score=True
)

API Reference

Complete API documentation for integrating monitoring intelligence into your applications.

Monitoring Deployment Endpoint

POST /v1/monitoring/deploy

Deploy intelligent monitoring configuration with AI-optimized settings.

Request Body:

{
  "infrastructure": {
    "providers": ["aws", "kubernetes"],
    "services": ["web", "database", "cache"],
    "auto_discover": true
  },
  "monitoring_config": {
    "enable_predictions": true,
    "prediction_models": ["anomaly", "trend", "seasonal"],
    "confidence_threshold": 0.8,
    "smart_thresholds": true
  },
  "alert_configuration": {
    "notification_channels": [
      {
        "type": "slack",
        "webhook": "https://hooks.slack.com/...",
        "urgency": ["critical", "warning"]
      },
      {
        "type": "email",
        "recipients": ["ops@company.com"],
        "urgency": ["critical"]
      }
    ],
    "escalation_policy": {
      "enabled": true,
      "escalation_time": "15m"
    }
  },
  "optimization_goals": {
    "reduce_alert_noise": true,
    "improve_coverage": true,
    "enable_auto_remediation": false
  }
}

Response:

{
  "deployment_id": "mon-deploy-123",
  "status": "completed",
  "summary": {
    "services_monitored": 8,
    "alerts_configured": 34,
    "predictive_alerts": 12,
    "dashboards_created": 5
  },
  "monitoring_configuration": {
    "prometheus_rules": 28,
    "grafana_dashboards": [
      "infrastructure-overview",
      "application-performance",
      "predictive-insights"
    ],
    "alert_rules": [
      {
        "name": "High CPU Usage Prediction",
        "type": "predictive",
        "service": "web-tier",
        "prediction_window": "2h",
        "confidence": 0.85
      }
    ]
  },
  "health_baseline": {
    "overall_score": 87,
    "performance_score": 92,
    "reliability_score": 84,
    "efficiency_score": 85
  },
  "insights": [
    {
      "type": "optimization",
      "title": "Memory utilization trending upward",
      "service": "web-tier",
      "prediction": "Memory exhaustion predicted in 6 hours",
      "recommendation": "Consider scaling up memory allocation",
      "confidence": 0.89
    }
  ]
}

Troubleshooting

Common issues and solutions when implementing intelligent monitoring and predictive alerting.

Common Issues

High False Positive Rate

Predictive alerts generating too many false positives

Solutions:

Increase confidence threshold for predictive alerts
Extend baseline learning period for better accuracy
Enable smart thresholds to adapt to normal variations
Fine-tune prediction models with recent data

Missing Monitoring Coverage

Auto-discovery not finding all infrastructure components

Solutions:

Verify cloud provider credentials and permissions
Check network connectivity to target systems
Manually add missing services to configuration
Update discovery patterns to include new resources

Performance Impact

Monitoring intelligence causing performance overhead

Solutions:

Adjust monitoring frequency for less critical services
Use sampling for high-volume metrics collection
Optimize prediction model execution schedules
Scale monitoring infrastructure resources

Monitoring Intelligence Documentation Complete!

Ready to revolutionize your monitoring? Start your free intelligence assessment today and discover how AI can predict issues before they impact your users.

AI MonitorIQ

Intelligent Setup

Predictive Alerts

Smart Insights

Auto-Optimization

Installation

System Requirements

Install via Package Manager

Monitoring Stack Integration

Quick Start

1. Discover Infrastructure

2. Deploy Monitoring

3. Monitor and Optimize

Configuration

Basic Configuration

Predictive Alerts

Anomaly Prediction

Trend Analysis

Seasonal Patterns

Correlation Analysis

Environment Variables

Basic Usage

Monitoring Commands

CLI Commands Reference

deploy

predict

Best Practices

Intelligent Monitoring Strategy

Monitoring Templates

Available Templates

Web Application Stack

Microservices Platform

Data Pipeline

Serverless Functions

Health Insights

Insight Categories

Performance Insights

Reliability Insights

Efficiency Insights

Generate Health Insights

API Integration

REST API

Python SDK

API Reference

Monitoring Deployment Endpoint

POST /v1/monitoring/deploy

Request Body:

Response:

Troubleshooting

Common Issues

High False Positive Rate

Missing Monitoring Coverage

Performance Impact

Monitoring Intelligence Documentation Complete!

AI MonitorIQ

Intelligent Setup

Predictive Alerts

Smart Insights

Auto-Optimization

Installation

System Requirements

Install via Package Manager

Monitoring Stack Integration

Quick Start

1. Discover Infrastructure

2. Deploy Monitoring

3. Monitor and Optimize

Configuration

Basic Configuration

Predictive Alerts

Anomaly Prediction

Trend Analysis

Seasonal Patterns

Correlation Analysis

Environment Variables

Basic Usage

Monitoring Commands

CLI Commands Reference

deploy

predict