AI-Powered IT Monitoring: Predictive Analytics for Infrastructure
International Workshop on AI-Driven Infrastructure Monitoring, Risk Detection & Automation
About This Course
“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.
Aim
To enable participants to leverage Artificial Intelligence and Predictive Analytics for monitoring IT infrastructure, optimizing system performance, preventing outages, and improving operational resilience through real-time anomaly detection and trend forecasting.
Workshop Objectives
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Teach how to apply AI to infrastructure monitoring and alerting
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Enable development of ML-based forecasting and risk detection models
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Train participants in real-time data processing and IT observability stacks
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Promote proactive incident prevention and capacity planning using AI
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Help organizations transition from reactive to intelligent, automated IT operations
Workshop Structure
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
- Build an end-to-end AI Monitoring Pipeline:
🛠️ Tools Used:
Slack API, Webhook, Shell Scripting, AWS CloudWatch, Grafana, Python, Cron Jobs
Who Should Enrol?
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System administrators and IT operations teams
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Data engineers and DevOps professionals
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AI/ML engineers exploring AIOps and automation
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Network security analysts and cloud infrastructure architects
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Tech leaders seeking proactive infrastructure risk mitigation
Important Dates
Registration Ends
05/22/2025
IST 4 PM
Workshop Dates
05/22/2025 – 05/24/2025
IST 5 PM
Workshop Outcomes
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Build and deploy predictive analytics for infrastructure monitoring
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Set up anomaly detection pipelines integrated with monitoring tools
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Analyze and visualize metrics, logs, and events using AI-enhanced dashboards
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Understand how to reduce downtime, MTTR, and false alerts
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Gain certification validating your skills in AIOps and predictive monitoring
Meet Your Mentor(s)
Fee Structure
Student Fee
₹1999 | $55
Ph.D. Scholar / Researcher Fee
₹2999 | $65
Academician / Faculty Fee
₹3999 | $75
Industry Professional Fee
₹5999 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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