Course Outline
Introduction to AIOps
Origins and development of AIOps
The significance of AIOps in contemporary IT
AIOps vs. IT Operations Analytics – distinguishing features
Essential technologies and foundational concepts
The lifecycle of AIOps systems
Associated practices and methodologies
AIOps in an Organizational Setting
Primary drivers and influencing elements
Integration with DevOps practices
The role of AIOps within 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
Big Data characteristics 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. conventional analytics
ML models and their AIOps applications
The future trajectory of AI in IT operations
Comparing ML with data analytics strategies
AIOps and Operational Metrics
Crucial operational metrics for IT environments
Significant indicators across diverse systems
SLA, SLO, and KPI – definitions and application
Incident-focused metrics: detection and categorization
Time-based metrics: MTTD, MTBF, MTTA, MTTR
Overseeing 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
Assessing the Impact of AIOps
Primary AIOps metrics for IT operations
Synergy between AIOps, DevOps, and SRE
Enhancing AI precision through AIOps
Improving system observability
Monitoring the operational impact of AIOps
Aligning AIOps metrics with DORA indicators
Deploying AIOps within the Organization
Steering clear of common implementation pitfalls
Ethics and machine learning in AIOps
Deployment pathways and strategic planning
Data integrity and process synchronization
Organizational culture and enabling practices
Data regulations and compliance requirements
Managing ML model inaccuracies
Privacy and user data safeguarding
Requirements
A foundational grasp of IT terminology and prior experience in working with information technologies are required.
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
