AIOps Certified Professional

Course Price at
₹ 49,999
[Fixed — No Negotiations]
4.8/5Rating
100 hrs4 Hrs/Day
4036Participants
20Tools

AIOps Certified Professional Training

The AIOps Certified Professional certification is tailored for IT and operations professionals seeking to master the integration of artificial intelligence into IT operations. This certification covers the use of AI and machine learning to automate and enhance IT processes, enabling faster issue detection, predictive maintenance, and optimized performance. Participants learn to implement AIOps tools and techniques that monitor, analyze, and act on large volumes of data generated by IT infrastructure and applications.

What is AIOps Certified Professional?

The AIOps Certified Professional certification is a specialized program equipping IT and operations professionals with the skills to leverage artificial intelligence in IT operations to streamline and optimize complex IT environments. The certification covers essential AIOps principles, focusing on using AI and machine learning to automate monitoring, detect anomalies, and predict issues before they impact the system. Designed for IT managers, operations engineers, DevOps practitioners, and data scientists, the program provides hands-on training in AIOps tools for handling large-scale data analysis, enhancing observability, and reducing manual intervention.

Course Feature

  • Comprehensive Curriculum: Covers essential AIOps concepts — anomaly detection, root cause analysis, predictive analytics, and automation with real-world applications.
  • Hands-On Labs: Practical labs with popular AIOps tools and frameworks including Splunk, Elastic Stack, IBM Watson AIOps, and machine learning libraries.
  • Expert-Led Training: Instructors specializing in AIOps provide a blend of theoretical understanding and real-world operational insights.
  • Live Project Work: End-to-end AIOps projects applying automation and AI to real-world challenges in monitoring, incident response, and performance optimization.
  • Case Studies: Industry examples demonstrating benefits and implementation challenges across various AIOps use cases.
  • Certification Exam Preparation: Mock exams and practice questions to prepare confidently for the AIOps Certified Professional exam.
  • Flexible Learning Options: Online and in-person formats to accommodate different learning preferences and schedules.
  • Community Access: Professional network of AIOps practitioners for ongoing support, knowledge-sharing, and industry connections.

Training Objectives

  • Master AIOps Principles: Deep understanding of AI-driven IT operations culture, principles, and the full observability lifecycle.
  • Anomaly Detection: Implement ML-based anomaly detection systems that surface issues before they impact users.
  • Root Cause Analysis: Use event correlation and topology mapping to automate root cause identification.
  • Predictive Analytics: Build predictive models for capacity planning, failure prevention, and proactive IT management.
  • Automated Remediation: Design and implement automated incident response workflows to reduce MTTR.
  • Log Intelligence: Apply NLP and ML to log analysis for pattern recognition and alert noise reduction.
  • AIOps Platform Mastery: Hands-on expertise with Dynatrace, Splunk, Moogsoft, IBM Watson AIOps, and BigPanda.
  • SLO Monitoring: Integrate AIOps into SRE workflows for SLO-driven alerting and error budget management.
  • Security Monitoring: Configure threat detection and security policy automation within AIOps platforms.
  • Exam Readiness: Prepare for the certification exam with structured mock exams and scenario-based practice.
Target Audience

This program is designed for IT managers, operations engineers, DevOps practitioners, SREs, and data scientists who want to enhance their skills in AI-driven IT operations. It also benefits cloud engineers, platform engineers, and incident response leads who manage high-demand applications and seek to reduce alert noise, accelerate incident resolution, and drive efficiency through intelligent automation. Anyone aiming to advance in roles such as AIOps Engineer, IT Operations Manager, or Observability Engineer will find this certification invaluable.

Training Methodology
  • Hands-on labs with real-world AIOps tool configurations
  • Instructor-led sessions and live demos of anomaly detection and automation
  • Self-paced video tutorials and downloadable resources
  • Mock exams and practice tests simulating the certification environment
  • Capstone project: end-to-end AIOps pipeline with data collection, ML models, and automation
  • Peer collaboration forums and community Q&A
Training Materials
  • Detailed course slides and eBooks covering the full AIOps curriculum
  • Command reference cheat sheets for Splunk, Elastic, Prometheus, and OTel
  • Step-by-step lab guides for anomaly detection, root cause analysis, and automation
  • Video tutorials demonstrating platform configuration and pipeline setup
  • Mock exams and practice tests in certification format
  • Interactive online labs and cloud sandbox environments
  • Case studies from AIOps implementations across telecom, fintech, and cloud industries
  • Comprehensive resource library: documentation, troubleshooting guides, and community forums
  • Capstone project guide for implementing a full AIOps solution
  • Discussion forums and peer networks for ongoing learning support

Agenda of AIOps Certified Professional

  • Overview of AIOps: Definition, Benefits, and Use Cases
  • AIOps in Modern IT Operations: Importance and Industry Adoption
  • Key Components: Machine Learning, Big Data, and Automation
  • Hands-On: Setting Up an AIOps Environment and Data Pipelines

  • Data Sources in AIOps: Logs, Metrics, Events, and Traces
  • Data Collection and Integration with AIOps Tools
  • Data Normalization and Cleaning Techniques
  • Hands-On: Setting Up Data Pipelines for AIOps

  • Introduction to Machine Learning Models in AIOps
  • Anomaly Detection Techniques and Algorithms
  • Root Cause Analysis and Event Correlation
  • Hands-On: Applying ML Models to Detect Anomalies

  • Automation in IT Operations: Incident Management and Resolution
  • Rule-Based vs. ML-Based Automation
  • Workflow Automation Tools and Best Practices
  • Hands-On: Implementing Automated Incident Responses

  • Predictive Models: Capacity Planning and Failure Prediction
  • Monitoring Trends and Patterns for Proactive Management
  • Hands-On: Building Predictive Models for Downtime Prevention

  • NLP in AIOps: Use Cases in Ticketing, Logs, and Alerts
  • Text Analysis, Clustering, and Classification
  • Automating Log Analysis and Incident Categorization
  • Hands-On: Using NLP to Analyze IT Tickets and Logs

  • Leading AIOps Platforms: Moogsoft, Splunk, IBM Watson AIOps, Dynatrace, BigPanda
  • Tool Selection and Integration with ITSM Systems
  • Best Practices for Implementing and Scaling AIOps
  • Hands-On: Configuring an AIOps Platform and Integrating with ITSM

  • Security Considerations for AIOps Implementations
  • Data Privacy, Compliance, and Ethical AI
  • Implementing Security Monitoring and Threat Detection
  • Hands-On: Configuring Security Policies in AIOps

  • Hybrid and Multi-Cloud AIOps Strategies
  • AIOps for DevOps and CI/CD Pipelines
  • Leveraging AIOps for Business Insights and Operational Intelligence
  • Hands-On: Configuring AIOps in Cloud Environments

  • Capstone Project: Building a Complete AIOps Pipeline
  • Exam Tips and Strategies for Success
  • Mock Exams and Practice Questions
  • Final Q&A and Certification Guidance

PROJECT

In the AIOps Certified Professional course, participants will work on 3 real-time scenario-based projects covering alert noise reduction, predictive anomaly detection, and automated incident remediation. Projects are designed to simulate production AIOps environments and give hands-on experience from data pipeline setup to ML model deployment and monitoring.

INTERVIEW

As part of this program, you will receive a complete interview preparation kit — crafted from 200+ years of combined industry experience and insights from nearly 10,000 DevOpsSupport learners worldwide. The kit covers AIOps-specific interview scenarios, tool configuration questions, and behavioral interviews for IT operations roles.

Our Course in Comparison

FeaturesDevOpsSupportOthers
AI & ML Coverage (AIOps-Specific)
Hands-On AIOps Platform Labs
Faculty Profile Check
Lifetime Technical Support
Lifetime LMS Access
20+ AIOps Tools Coverage
Interview Kit (Q&A)
Training Notes
Step-by-Step Web-Based Tutorials
Training + Additional Videos

Frequently Asked Questions

This certification validates expertise in applying AI and machine learning to automate and optimize IT operations, improving system reliability and reducing manual intervention.

It's ideal for IT managers, operations engineers, DevOps practitioners, SREs, and data scientists who want to enhance their skills in AI-driven IT operations and automation.

You'll learn to implement AIOps practices such as anomaly detection, predictive analytics, root cause analysis, and automated remediation within IT environments.

Basic knowledge of IT operations and data analysis is recommended. Familiarity with monitoring tools and scripting helps but is not required.

The course covers Splunk, Elastic Stack, IBM Watson AIOps, Dynatrace, Moogsoft, BigPanda, PagerDuty, and various machine learning libraries.

The exam includes practical tasks assessing your ability to apply AIOps concepts, implement automation, and perform data-driven analysis in simulated IT environments.

The certification is valid for 3 years, after which recertification is required to stay updated with new AIOps practices and technologies.

The training includes instructor-led sessions, hands-on labs, real-world projects, case studies, and mock exams to fully prepare for the certification.

Yes, retake options are available, often at a reduced cost or included with the initial registration.

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