Course Outline
Introduction to AIOps
The origins and development of AIOps
The significance of AIOps in modern IT
AIOps versus IT Operations Analytics – primary distinctions
Foundational technologies and concepts
The AIOps system lifecycle
Associated practices and methodologies
AIOps in an Organizational Setting
Primary drivers and influencing factors
Integration with DevOps
The function of AIOps in Site Reliability Engineering (SRE)
AIOps and IT security considerations
Data, telemetry, and system complexity
A new framework for assessing system health
Core Technologies – Data
Defining Big Data
The 5 Vs of Big Data
Big Data characteristics in AIOps
Data sources and categories in AIOps environments
Data diversity and processing obstacles
Core Technologies – Machine Learning (ML)
AI, ML, and their relevance to AIOps
Supervised versus unsupervised learning in AIOps
Machine learning versus traditional analytics
ML models and their use in AIOps
The future trajectory of AI in IT operations
Comparing ML with data analytics methods
AIOps and Operational Metrics
Critical operational metrics for IT environments
Key indicators across different systems
SLA, SLO, and KPI – definitions and applications
Incident-related metrics: identification and categorization
Time-based metrics: MTTD, MTBF, MTTA, MTTR
Managing service level agreements
Use Cases and Shifting Organizational Mindsets
Transitioning from reactive to proactive operations
Traits of a reactive IT operations model
Moving from deterministic to probabilistic methods
Practical AIOps use cases
Organizational transformation driven by AIOps
Analyzing the past to predict the future
Assessing the Impact of AIOps
Key AIOps metrics for IT operations
The synergy between AIOps, DevOps, and SRE
Enhancing AI precision through AIOps
Improving system observability
Monitoring AIOps impact on operations
Aligning AIOps metrics with DORA indicators
Implementing AIOps within the Organization
Avoiding frequent pitfalls
Ethics and machine learning in AIOps
Deployment paths and strategies
Data integrity and process alignment
Organizational culture and supportive practices
Data regulations and compliance
Managing ML model errors
Privacy and user data security
Requirements
A foundational grasp of IT terminology and hands-on 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
