Course Outline
Introduction to AIOps
Origins and developmental trajectory of AIOps
The significance of AIOps in contemporary IT
Distinguishing features between AIOps and IT Operations Analytics
Foundational technologies and concepts
The AIOps system lifecycle
Associated practices and methodologies
AIOps in an Organizational Setting
Primary drivers and influencing elements
Alignment with DevOps practices
The contribution of AIOps to Site Reliability Engineering (SRE)
AIOps considerations regarding IT security
Data, telemetry, and system intricacy
A novel framework for assessing system health
Core Technologies – Data
Defining Big Data
The 5 Vs of Big Data
Attributes of Big Data within AIOps contexts
Origins and classifications of data in AIOps environments
Data heterogeneity and processing obstacles
Core Technologies – Machine Learning (ML)
AI, ML, and their functions in AIOps
Supervised versus unsupervised learning in AIOps
Machine learning compared to conventional analytics
ML models and their deployment in AIOps
The prospective role of AI in IT operations
Comparing ML with data analytics techniques
AIOps and Operational Metrics
Essential operational indicators for IT environments
Critical metrics across diverse systems
Definitions and application of SLA, SLO, and KPI
Incident-specific metrics: identification and categorization
Time-related metrics: MTTD, MTBF, MTTA, MTTR
Oversight of service level agreements
Use Cases and Organizational Mindset Shift
Transitioning from reactive to proactive operations
Features of a reactive IT operations model
Shift from deterministic to probabilistic methodologies
Practical AIOps use cases
Organizational transformation enabled by AIOps
Analyzing history to forecast future outcomes
Measuring the Impact of AIOps
Pivotal AIOps metrics for IT operations
Interplay between AIOps, DevOps, and SRE
Boosting AI precision through AIOps
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
Ethics and machine learning within AIOps
Deployment pathways and strategic plans
Data integrity and process synchronization
Organizational culture and enabling practices
Data governance and regulatory compliance
Managing errors in ML models
Privacy and safeguarding user data
Requirements
Familiarity with fundamental IT terminology and practical experience in working with information systems.
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
