AI-Powered IT Monitoring: Predictive Analytics for Infrastructure | Live Workshop

AI-Powered IT Monitoring: Predictive Analytics for Infrastructure

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Course Overview

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Workshop Registration End Date :2025-05-22

Mentor Based

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AI-Powered IT Monitoring: Predictive Analytics for Infrastructure

Professional Certification Program

🎥

FormatLive + Recorded

📅

Duration8 Weeks

📜

CertificationVerified

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Program Syllabus

Module 1

About

“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.

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Module 2

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.

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Module 3

Workshop Objectives

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

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Module 4

Workshop Structure

Module 5

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

🎯 Focus: AI Foundations | Telemetry Setup | Metric Collection

Module 6

🔹 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

Module 7

🔧 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

Module 8

🛠️ Tools Used:

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

Module 9

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

🎯 Focus: Data Modeling | Forecasting | Detection

Module 10

🔹 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)

Module 11

🔧 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

Module 12

🛠️ Tools Used:

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

Module 13

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

🎯 Focus: Integration | Auto-Remediation | DevOps Alignment

Module 14

🔹 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

Module 15

🔧 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

Module 16

🛠️ Tools Used:

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

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Module 17

Intended For

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

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Module 18

Important Dates

Module 19

Registration Ends

2025-05-22
Indian Standard Timing 4 PM

Module 20

Workshop Dates

2025-05-22 to 2025-05-24
Indian Standard Timing 5 PM

Module 21

Workshop Outcomes

Build and deploy predictive analytics for infrastructure monitoring
Set up anomaly detection pipelines integrated with monitoring tools
Analyze and visualize metrics, logs, and events using AI-enhanced dashboards
Understand how to reduce downtime, MTTR, and false alerts
Gain certification validating your skills in AIOps and predictive monitoring

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Module 22

Mentor Profile

Gurpreet Kaur

Assistant Professor

Module 23

Fee Structure

Module 24

Student Fee

INR. 1999
USD. 55

Module 25

Ph.D. Scholar / Researcher Fee

INR. 2999
USD. 65

Module 26

Academician / Faculty Fee

INR. 3999
USD. 75

Module 27

Industry Professional Fee

INR. 5999
USD. 95

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of Currencies

Module 28