The AIOps Foundation Certification Training Course provides participants with a foundational understanding of Artificial Intelligence for IT Operations (AIOps) and how artificial intelligence, machine learning, automation, and data analytics are applied to modern IT operations.
The course explores the evolution of AIOps, its key concepts and technologies, the relationship between AIOps and DevOps/SRE practices, and its role in monitoring, event management, observability, incident response, automation, and continuous improvement. Participants will learn how AIOps platforms collect and analyze large volumes of operational data to identify patterns, detect anomalies, correlate events, predict potential problems, and automate appropriate responses.
The training also covers organizational considerations, implementation approaches, governance, challenges, and practical use cases to help participants understand how AIOps can improve service reliability, operational efficiency, and business outcomes.
This course is suitable for professionals seeking foundational AIOps knowledge and preparing for an AIOps Foundation-level certification examination.
Duration 2 Days – 14 hrs.
Objectives
- Explain the fundamental concepts, principles, and business drivers of AIOps.
- Describe the evolution of IT operations and the need for AIOps.
- Understand the role of artificial intelligence and machine learning in IT operations.
- Identify common data sources used by AIOps platforms.
- Explain how AIOps supports monitoring, observability, event management, and incident management.
- Understand anomaly detection, event correlation, noise reduction, and predictive analytics.
- Explain how automation and orchestration are incorporated into AIOps.
- Describe the relationship between AIOps, DevOps, Site Reliability Engineering (SRE), and IT Service Management (ITSM).
- Identify common AIOps use cases and organizational benefits.
- Understand key considerations for selecting and implementing AIOps capabilities.
- Recognize organizational, cultural, data, security, and governance challenges associated with AIOps.
- Understand approaches for measuring AIOps effectiveness and business value.
- Apply foundational AIOps concepts to common IT operations scenarios.
- Prepare for an AIOps Foundation-level certification examination.
Target Audience
- IT Operations Professionals
- DevOps Engineers and Practitioners
- Site Reliability Engineers (SREs)
- System Administrators
- Network Administrators and Engineers
- Cloud Engineers and Cloud Operations Professionals
- IT Service Management Professionals
- Service Desk and Technical Support Professionals
- IT Operations Managers
- Infrastructure Engineers
- Application Support Professionals
- Monitoring and Observability Specialists
- Automation Engineers
- IT Architects and Solution Architects
- IT Managers and Technical Team Leaders
- Data and Analytics Professionals supporting IT operations
- Professionals involved in digital transformation and IT modernization
- Individuals preparing for an AIOps Foundation certification
Prerequisites
- Basic understanding of IT infrastructure and IT operations
- General knowledge of applications, servers, networks, and cloud environments
- Basic awareness of IT Service Management (ITSM) concepts
- Familiarity with DevOps concepts is helpful but not required
- Basic awareness of artificial intelligence, machine learning, or data analytics is beneficial but not mandatory
- No programming or advanced data science experience is required.
Course Outline
Day 1 – AIOps Fundamentals, Technologies, Data, and Operational Intelligence
Module 1: Introduction to AIOps
- Definition and purpose of AIOps
- Evolution of traditional IT operations
- Challenges of modern IT environments
- Increasing complexity of cloud, hybrid, and distributed systems
- Why traditional monitoring approaches are insufficient
- Business and operational drivers for AIOps
- Key characteristics of an AIOps platform
- Benefits and expected outcomes of AIOps adoption
Module 2: Artificial Intelligence and Machine Learning in IT Operations
- Artificial intelligence fundamentals in the context of IT operations
- Machine learning concepts relevant to AIOps
- Supervised and unsupervised learning concepts
- Pattern recognition
- Classification and clustering
- Anomaly detection
- Predictive analytics
- Natural language processing in IT operations
- Generative AI and emerging applications in IT operations
- Human intelligence versus machine-assisted operations
Module 3: AIOps Data and Data Management
- Importance of data in AIOps
- Structured and unstructured operational data
- Metrics, events, logs, traces, and topology data
- Application and infrastructure telemetry
- Network and cloud data
- ITSM and service desk data
- Data ingestion and normalization
- Data quality and data enrichment
- Real-time and historical data analysis
- Managing large volumes and velocity of operational data
Module 4: Monitoring, Observability, and AIOps
- Traditional monitoring versus observability
- Understanding metrics, logs, and traces
- Infrastructure and application monitoring
- Service and business observability
- Dynamic infrastructure discovery
- Service dependency mapping
- Establishing operational baselines
- Identifying abnormal system behavior
- Using AIOps to enhance observability
- Moving from reactive to proactive operations
Module 5: Event Management and Intelligent Correlation
- Understanding events, alerts, and incidents
- Challenges of alert and event overload
- Event aggregation
- Event deduplication
- Noise reduction and alert suppression
- Event correlation
- Identifying related operational events
- Root cause analysis
- Impact analysis
- Prioritization based on business and service impact
- Reducing Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR)
Day 2 – Automation, AIOps Practices, Implementation, Governance, and Certification Preparation
Module 6: AIOps Automation and Intelligent Remediation
- Role of automation in AIOps
- From detection to automated response
- Workflow automation
- Automated incident enrichment
- Automated diagnostics
- Automated remediation
- Runbooks and intelligent automation
- Orchestration across IT tools
- Closed-loop automation
- Human-in-the-loop approaches
- Benefits and risks of autonomous operations
Module 7: AIOps, DevOps, SRE, and ITSM
- Relationship between AIOps and DevOps
- AIOps within CI/CD environments
- AIOps and Site Reliability Engineering
- Service Level Indicators (SLIs)
- Service Level Objectives (SLOs)
- Error budgets and reliability
- AIOps and IT Service Management
- Incident, problem, and change management
- Supporting continuous improvement
- Breaking down operational silos
- Collaboration between development and operations teams
Module 8: AIOps Use Cases and Business Value
- Proactive incident detection
- Intelligent alert management
- Anomaly detection
- Root cause identification
- Predictive failure detection
- Capacity and performance management
- Resource optimization
- Application performance management
- Network operations
- Cloud and hybrid infrastructure operations
- Service desk intelligence
- Automated incident resolution
- Improving availability and service reliability
- Cost optimization and operational efficiency
Module 9: Implementing AIOps in the Organization
- Assessing AIOps readiness
- Identifying business and operational objectives
- Selecting suitable AIOps use cases
- Understanding the existing IT operations ecosystem
- Data readiness and integration requirements
- Selecting AIOps tools and platforms
- Proof of concept and pilot implementation
- Phased AIOps adoption
- Integrating AIOps with existing tools
- Defining roles and responsibilities
- Scaling AIOps across the enterprise
- Continuous improvement of AIOps capabilities
Module 10: AIOps Governance, Risks, and Challenges
- Data quality and availability challenges
- Integration and interoperability challenges
- Organizational and cultural resistance
- Skills and competency requirements
- AI model accuracy and reliability
- Transparency and explainability
- Automation risks
- Security and privacy considerations
- AI governance
- Human oversight and accountability
- Ethical and responsible use of AI
- Managing organizational change
Module 11: Measuring AIOps Success
- Establishing AIOps success criteria
- Operational performance indicators
- Mean Time to Detect (MTTD)
- Mean Time to Acknowledge (MTTA)
- Mean Time to Resolve/Repair (MTTR)
- Incident and alert reduction
- Service availability and reliability
- Automation rate
- Operational productivity
- Cost and resource optimization
- Measuring business value and return on investment
- Continuous measurement and optimization
Module 12: AIOps Foundation Certification Preparation
- Review of key AIOps terminology
- Review of foundational concepts
- AIOps technologies and capabilities
- Data, analytics, and machine learning concepts
- AIOps use cases and operational practices
- Implementation and organizational considerations
- Governance and responsible AIOps
- Key concepts to remember for the certification examination
- Practice certification-style questions
- Final knowledge review
