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Monitoring for Infrastructure

AI-Powered IT Monitoring: Predictive Analytics for Infrastructure

Original price was: USD $99.00.Current price is: USD $59.00.

International Workshop on AI-Driven Infrastructure Monitoring, Risk Detection & Automation

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Course Overview

AI-Powered IT Monitoring: Predictive Analytics for Infrastructure is a hands-on, international course designed to help IT professionals integrate 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. This course will empower you to move from reactive to proactive IT operations, optimizing system performance and improving operational resilience.

Course Objective

The goal of this course is to teach participants how to leverage AI and predictive analytics to monitor IT infrastructure, prevent outages, and optimize system performance using real-time anomaly detection and trend forecasting.

Learning Outcomes

  • Learn how to apply AI to IT infrastructure monitoring and alerting.
  • Gain hands-on experience with ML-based forecasting and risk detection models.
  • Build expertise in real-time data processing and IT observability stacks.
  • Implement proactive incident prevention and capacity planning using AI.
  • Transition from reactive to intelligent, automated IT operations.

Course Structure

📅 Module 1: Architecture & Instrumentation of AI-Powered Monitoring Systems

  • Focus: AI Foundations | Telemetry Setup | Metric Collection
  • Topics Covered:
    • Introduction to AIOps (AI in IT Operations)
    • Traditional vs predictive monitoring approaches
    • Key components of IT infrastructure (cloud, on-prem, hybrid)
    • Data sources: Logs, metrics, events, traces
    • The 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

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

  • Focus: Data Modeling | Forecasting | Detection
  • Topics Covered:
    • Data preprocessing for monitoring (time windows, lag features, trends)
    • Predictive analytics techniques (ARIMA, Facebook Prophet, LSTM)
    • Anomaly detection (Z-score, Isolation Forest, Autoencoders)
    • Evaluation metrics (MAE, RMSE, Precision/Recall)
  • 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

📅 Module 3: Automation, Alerting, and Scalable AI Monitoring Pipelines

  • Focus: Integration | Auto-Remediation | DevOps Alignment
  • Topics Covered:
    • 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

Who Should Enrol?

  • 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 looking for proactive infrastructure risk mitigation

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Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

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