Course Outline
Introduction to AIOps
The origins and historical development of AIOps
The significance of AIOps in contemporary IT landscapes
Distinguishing AIOps from IT Operations Analytics
Fundamental technologies and underlying concepts
The complete lifecycle of an AIOps system
Associated practices and methodological frameworks
AIOps in an Organizational Setting
Primary drivers and environmental influences
Synergy between AIOps and DevOps
The contribution of AIOps to Site Reliability Engineering (SRE)
AIOps perspectives on IT security risks
Navigating data, telemetry, and systemic complexity
A new approach to assessing system health
Core Technologies – Data
Defining Big Data
The five V's of Big Data
Specific Big Data characteristics within AIOps
Identifying data sources and types in AIOps contexts
Challenges related to data diversity and processing
Core Technologies – Machine Learning (ML)
The roles of AI and ML in AIOps
Differentiating supervised and unsupervised learning in AIOps
Machine learning versus conventional analytics
Application of ML models in AIOps
Future trends of AI in IT operations
A comparative analysis of ML and data analytics methods
AIOps and Operational Metrics
Critical operational metrics for IT environments
Key indicators across diverse systems
Understanding and applying SLA, SLO, and KPI
Metrics related to incident detection and categorization
Time-centric metrics: MTTD, MTBF, MTTA, MTTR
Managing service level agreements effectively
Use Cases and Organizational Mindset Shift
Transitioning from reactive to proactive operations
Traits of a reactive IT operations model
Shifting from deterministic to probabilistic methods
Practical real-world applications of AIOps
Driving organizational change through AIOps
Analyzing historical data to forecast future trends
Measuring the Impact of AIOps
Essential AIOps metrics for IT operations
The combined effect of AIOps, DevOps, and SRE
Boosting AI accuracy via AIOps implementation
Improving system observability
Monitoring the operational impact of AIOps
Aligning AIOps metrics with DORA indicators
Implementing AIOps in the Organization
Steering clear of common implementation errors
Ethical considerations and machine learning in AIOps
Strategic pathways for implementation
Ensuring data quality and process harmony
Fostering organizational culture and supportive practices
Adhering to data regulations and compliance standards
Managing machine learning model inaccuracies
Safeguarding privacy and user data
Requirements
A foundational grasp of IT terminology and hands-on experience 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
