Course Outline
Introduction to AIOps
Origins and evolution of AIOps
The significance of AIOps in modern IT
AIOps vs. IT Operations Analytics – distinguishing features
Fundamental technologies and concepts
The AIOps system lifecycle
Associated practices and methodologies
AIOps in the Organizational Context
Primary drivers and influencing factors
Integration with DevOps
The role of AIOps in Site Reliability Engineering (SRE)
AIOps and IT security considerations
Data, telemetry, and system complexity
A new paradigm for assessing system health
Core Technologies – Data
Defining Big Data
The 5 Vs of Big Data
Characteristics of Big Data within AIOps
Data sources and types in AIOps environments
Data diversity and processing complexities
Core Technologies – Machine Learning (ML)
AI, ML, and their function in AIOps
Supervised vs. unsupervised learning in AIOps
Machine learning vs. traditional analytics
ML models and their deployment in AIOps
The future trajectory of AI in IT operations
Comparing ML with data analytics approaches
AIOps and Operational Metrics
Critical operational metrics for IT environments
Significant indicators across diverse systems
SLA, SLO, and KPI – definitions and application
Incident-related metrics: detection and categorization
Time-based metrics: MTTD, MTBF, MTTA, MTTR
Managing service level agreements
Use Cases and Organizational Mindset Shift
Transitioning from reactive to proactive operations
Traits of a reactive IT operations model
Shifting from deterministic to probabilistic approaches
Practical use cases of AIOps
Organizational transformation driven by AIOps
Analyzing the past to forecast the future
Measuring the Impact of AIOps
Primary AIOps metrics for IT operations
Synergy between AIOps, DevOps, and SRE
Enhancing AI accuracy through AIOps
Improving system observability
Monitoring AIOps impact on operations
Aligning AIOps metrics with DORA indicators
Implementing AIOps in the Organization
Avoiding common pitfalls
Ethics and machine learning in AIOps
Implementation pathways and strategies
Data quality and process alignment
Organizational culture and supporting practices
Data regulations and compliance
Addressing ML model errors
Privacy and user data protection
Requirements
Foundational knowledge of IT terminology and practical experience with information technologies.
Testimonials (2)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
