AI Network Monitoring
Go beyond static thresholds with AI-powered network monitoring. Learn to implement intelligent monitoring using Datadog AI, Splunk ITSI, Prometheus with ML extensions, and build smart alerting systems that reduce noise and catch real issues.
What You'll Learn
Master AI-enhanced monitoring tools and techniques for modern network operations.
Datadog AI Monitoring
Leverage Datadog Watchdog, anomaly detection, forecast monitors, and NPM for intelligent network visibility.
Splunk ITSI
Configure Splunk IT Service Intelligence for service-level monitoring, KPI tracking, and predictive analytics.
Prometheus + ML
Extend Prometheus with machine learning for anomaly detection, forecasting, and intelligent alerting at scale.
Smart Alerting
Design alerting systems with dynamic thresholds, correlation rules, and ML-based noise reduction.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
The evolution of network monitoring from polling to AI. Understanding why traditional thresholds fail and how AI solves it.
2. Datadog AI
Datadog Watchdog, anomaly monitors, forecast monitors, Network Performance Monitoring, and Network Device Monitoring.
3. Splunk ITSI
Setting up services, KPI definitions, glass tables, predictive analytics, and event analytics in Splunk ITSI.
4. Prometheus + ML
Adding ML capabilities to Prometheus: prophet integration, anomaly detection exporters, and custom ML recording rules.
5. Alerting
Designing intelligent alerting: dynamic baselines, composite alerts, SLO-based alerts, and on-call optimization.
6. Best Practices
Monitoring strategy design, tool selection criteria, multi-tool integration, and building a monitoring-as-code culture.
Prerequisites
- Experience with network monitoring (SNMP, syslog, or similar)
- Basic familiarity with at least one monitoring platform
- Understanding of network protocols and metrics
- Basic knowledge of AI/ML concepts (from earlier courses)