Course Outline
Introduction to AIOps for Government
Origins and evolution of AIOps for government
The importance of AIOps in modern IT operations for government
Key differences between AIOps and IT Operations Analytics
Core technologies and concepts for government use
AIOps system lifecycle in the public sector
Related practices and methodologies for government agencies
AIOps in the Organizational Context for Government
Key drivers and influencing factors for government organizations
Integration of AIOps with DevOps in government settings
The role of AIOps in Site Reliability Engineering (SRE) for government
AIOps and IT security concerns for government agencies
Data, telemetry, and system complexity in the public sector
A new paradigm for understanding system health in government IT
Core Technologies – Data for Government
What is Big Data in the context of government operations?
The 5 Vs of Big Data relevant to government agencies
Characteristics of Big Data in AIOps for government
Data sources and types in AIOps environments for government
Data diversity and processing challenges for government IT
Core Technologies – Machine Learning (ML) for Government
AI, ML, and their role in AIOps for government operations
Supervised vs. unsupervised learning in AIOps for government
Machine learning vs. traditional analytics in the public sector
Application of ML models in AIOps for government agencies
The future of AI in IT operations for government
Comparing ML with data analytics approaches in government
AIOps and Operational Metrics for Government
Key operational metrics for IT environments in government agencies
Important indicators across various systems for government operations
Definitions and usage of SLA, SLO, and KPI in government IT
Incident-related metrics: detection and classification for government
Time-based metrics: MTTD, MTBF, MTTA, MTTR for government operations
Managing service level agreements in government agencies
Use Cases and Organizational Mindset Shift for Government
Transition from reactive to proactive operations in government IT
Characteristics of a reactive IT operations model in the public sector
Moving from deterministic to probabilistic approaches in government
Real-world use cases of AIOps for government agencies
Organizational change driven by AIOps in government
Understanding the past and predicting the future with AIOps in government
Measuring the Impact of AIOps for Government
Key AIOps metrics for IT operations in government
Synergy between AIOps, DevOps, and SRE in government settings
Improving AI accuracy through AIOps for government
Enhancing system observability in government IT
Tracking AIOps impact on operations for government agencies
Connecting AIOps metrics with DORA indicators in the public sector
Implementing AIOps in the Organization for Government
Avoiding common pitfalls in AIOps implementation for government
Ethics and machine learning considerations in AIOps for government
Implementation paths and strategies for government agencies
Data quality and process alignment in government IT
Organizational culture and supporting practices for AIOps in government
Compliance with data regulations and standards for government
Handling ML model errors in government operations
Privacy and user data protection in AIOps for government agencies
Requirements
A foundational knowledge of IT terminology and practical experience working with information technologies for government.
Testimonials (1)
There were many practical exercises supervised and assisted by the trainer
