Course Outline
Introduction to AIOps
Historical origins and the evolution of AIOps
The significance of AIOps in contemporary IT infrastructure
Distinguishing AIOps from IT Operations Analytics – fundamental differences
Foundational technologies and core concepts
The lifecycle of an AIOps system
Associated practices and methodological frameworks
AIOps in the Organizational Context
Primary drivers and influential factors
Integration strategies with DevOps
The function of AIOps within Site Reliability Engineering (SRE)
AIOps and considerations for IT security
Data management, telemetry, and system complexity
A new paradigm for assessing system health
Core Technologies – Data
Definition and scope of Big Data
The 5 Vs characterizing Big Data
Attributes of Big Data within AIOps
Data sources and classifications in AIOps environments
Data diversity and associated processing complexities
Core Technologies – Machine Learning (ML)
AI, ML, and their application in AIOps
Supervised versus unsupervised learning in AIOps
Machine learning compared to traditional analytics
ML models and their utilization in AIOps
The future trajectory of AI in IT operations
Comparing ML with other data analytics approaches
AIOps and Operational Metrics
Essential operational metrics for IT environments
Critical indicators across diverse systems
SLA, SLO, and KPI – definitions and operational usage
Incident-related metrics: detection and categorization
Time-based metrics: MTTD, MTBF, MTTA, MTTR
Oversight of service level agreements
Use Cases and Organizational Mindset Shift
Transitioning from reactive to proactive operations
Characteristics of reactive IT operations models
Shift from deterministic to probabilistic methodologies
Practical real-world applications of AIOps
Organizational transformation driven by AIOps
Analyzing historical data to forecast future trends
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 the operational impact of AIOps
Aligning AIOps metrics with DORA indicators
Implementing AIOps in the Organization
Avoiding common implementation pitfalls
Ethics and machine learning considerations in AIOps
Implementation pathways and strategic planning
Data integrity and process alignment
Organizational culture and supporting operational practices
Data regulations and compliance requirements
Managing errors in ML models
Privacy standards and user data protection
Requirements
Fundamental knowledge of IT terminology and practical experience in the use of 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
