AI-Powered IT Monitoring: Predictive Analytics for Infrastructure

“AI-Powered IT Monitoring: Predictive Analytics for Infrastructure” is an international workshop focused on the integration of machine learning, time series forecasting, and AI-driven automation into modern IT monitoring systems. Participants will learn how to use AI to detect anomalies, predict failures, and enable self-healing responses across servers, networks, applications, and cloud environments.

• Teach how to apply AI to infrastructure monitoring and alerting

• Enable development of ML-based forecasting and risk detection models

• Train participants in real-time data processing and IT observability stacks

• Promote proactive incident prevention and capacity planning using AI

• Help organizations transition from reactive to intelligent, automated IT operations

Day 1: Architecture & Instrumentation of AI-Powered Monitoring Systems

🎯 Focus: AI Foundations | Telemetry Setup | Metric Collection

🔹 Theory Topics:

• Introduction to AI in IT Operations (AIOps)
• Traditional vs Predictive Monitoring Approaches
• Key Components of IT Infrastructure (Cloud, On-Prem, Hybrid)
• Data Sources: Logs, Metrics, Events, Traces
• Importance of Real-Time Observability

🔧 Hands-on Lab:

• Set up Prometheus for metric collection and Node Exporter for system telemetry
• Install and configure Grafana for real-time dashboarding
• Visualize system health KPIs: CPU, memory, disk I/O
• Simulate system load using stress-ng or Docker containers

🛠️ Tools Used:

Prometheus, Grafana, Node Exporter, Docker, stress-ng

Day 2: AI-Driven Analytics: Time Series Forecasting & Anomaly Detection

🎯 Focus: Data Modeling | Forecasting | Detection

🔹 Theory Topics:

• Data Preprocessing for Monitoring:
  • Time Windows, Lag Features, Trends
• Predictive Analytics Techniques:
  • Time Series Forecasting: ARIMA, Facebook Prophet, LSTM
  • Anomaly Detection: Z-score, Isolation Forest, Autoencoders
• Evaluation Metrics:
  • MAE, RMSE, Precision/Recall (for anomalies)

🔧 Hands-on Lab:

• Load system metric logs and apply forecasting using Prophet or LSTM (Keras)
• Build and validate an Isolation Forest anomaly detection model
• Integrate predictions with Grafana for real-time dashboards
• Trigger intelligent alerts using Alertmanager

🛠️ Tools Used:

Python, Pandas, Scikit-learn, Prophet, Keras, Grafana, Alertmanager

Day 3: Automation, Alerting, and Scalable AI Monitoring Pipelines

🎯 Focus: Integration | Auto-Remediation | DevOps Alignment

🔹 Theory Topics:

• Intelligent Alerting Systems:
  • Threshold vs Behavior-Based Alerts
  • Noise Reduction via Event Correlation & Suppression
• Automation Strategies:
  • Auto-Remediation & Self-Healing Systems
  • AIOps Workflow Design (Full-Stack)
• Use Case Spotlights:
  • AI in Cloud Monitoring (AWS CloudWatch + SageMaker)
  • AI for Edge & IoT Monitoring
  • AI-Enhanced Cybersecurity Detection

🔧 Hands-on Lab:

• Configure alerting via Slack, MS Teams, or Webhook APIs
• Develop an auto-remediation script (e.g., restart a failing service)
• Mini-Project:
  • Build an end-to-end AI Monitoring Pipeline:
    Metric Collection → Forecasting → Anomaly Detection → Alerting → Auto-Remediation
  • Deploy a live dashboard and demo anomaly recovery in real-time

🛠️ Tools Used:

Slack API, Webhook, Shell Scripting, AWS CloudWatch, Grafana, Python, Cron Jobs

• System administrators and IT operations teams

• Data engineers and DevOps professionals

• AI/ML engineers exploring AIOps and automation

• Network security analysts and cloud infrastructure architects

• Tech leaders seeking proactive infrastructure risk mitigation