Course Outline
Overview of AIOps
Historical development and evolution of AIOps
Strategic significance of AIOps for contemporary information technology infrastructure
Distinctive features differentiating AIOps from IT Operations Analytics
Foundational technologies and operational principles
The AIOps system lifecycle framework
Associated operational practices and methodologies
AIOps Within the Organizational Framework
Primary drivers and contextual influencing factors
Alignment with DevOps integration strategies
The function of AIOps in Site Reliability Engineering (SRE)
Implications for IT security and risk management
Challenges posed by data volume, telemetry, and system complexity
Emerging paradigms for assessing systemic health and stability
Core Technologies – Data Infrastructure
Definition and scope of Big Data
The five dimensions (5 Vs) of Big Data characteristics
Specific attributes of Big Data within AIOps contexts
Identification and classification of data sources in AIOps ecosystems
Addressing challenges related to data heterogeneity and processing requirements
Core Technologies – Machine Learning (ML)
The interplay between Artificial Intelligence, Machine Learning, and AIOps
Application of supervised versus unsupervised learning techniques in AIOps
Comparative analysis of machine learning against traditional analytics methods
Utilization of ML models for specific AIOps functions
Projected advancements in AI applications for IT operations
Evaluation of machine learning relative to broader data analytics strategies
AIOps and Operational Performance Metrics
Essential operational metrics for evaluating IT environments
Critical performance indicators across diverse system architectures
Definitions and applications of Service Level Agreements (SLA), Service Level Objectives (SLO), and Key Performance Indicators (KPI)
Metrics governing incident detection and classification procedures
Temporal performance metrics: Mean Time to Detect (MTTD), Mean Time Between Failures (MTBF), Mean Time to Acknowledge (MTTA), and Mean Time to Resolve (MTTR)
Strategies for managing and maintaining service level agreements
Operational Use Cases and Cultural Transformation
Transitioning from reactive remediation to proactive operations
Defining attributes of traditional, reactive IT operations models
Shifting from deterministic rules to probabilistic forecasting methodologies
Documented real-world implementations and applications of AIOps
Organizational change management facilitated by AIOps adoption
Leveraging historical data for predictive analytics and future state modeling
Evaluating the Impact of AIOps
Primary performance indicators for assessing AIOps effectiveness in IT operations
Interoperability between AIOps, DevOps, and SRE frameworks
Mechanisms for enhancing the accuracy of AI models through AIOps feedback loops
Advancements in comprehensive system observability
Methods for monitoring and quantifying operational improvements driven by AIOps
Alignment of AIOps metrics with DevOps Research and Assessment (DORA) performance indicators
Strategic Implementation of AIOps in Government Operations
Mitigation of common implementation risks
Ethical considerations and governance of machine learning in AIOps systems
Recommended pathways and strategic approaches for deployment
Ensuring data integrity and alignment with established operational processes
Cultivating organizational culture and supportive practices for successful adoption
Compliance with federal data regulations and security standards
Protocols for managing and correcting machine learning model discrepancies
Safeguarding user privacy and protecting sensitive government data
Requirements
Fundamental comprehension of IT vocabulary and practical experience deploying information technology solutions for government operations.
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
