New AIOps Foundation

The aim of the training is to introduce the basic concepts and solutions of AIOps, as well as its practical areas of use. During the training, participants will learn about data analysis, machine learning models and closely related areas of digital transformation, MLOps, DevOps, Site Reliability, AIOps, and other related topics. AIOps within an organization, and the benefits and challenges of implementing artificial steps for successful integration of artificial intelligence.

COURSE OBJECTIVES

• Describe the origins, evolution and current state of development of AIOps
• Review the basic concepts and principles of AIOps
• Interpret the relationship between AIOps and MLOps
• To understand the skills and mindset required within the organisation to implement AIOps
• Understand and present the general challenges and opportunities of AIOps

The training will also help you to prepare for the PeopleCert AIOPs Foundation exam, which can be taken after the training in PeopleCert's online system. The training fee does not include the exam fee.

The training is recommended primarily for managers (IT manager, project manager, development manager, technology manager) who want to learn about the application and implementation of artificial intelligence solutions and its IT service management process in their company or organization. The training is also ideal for those who will be involved in the development, design and IT support of AI-based business solutions in the future and who want to acquire the technological and business basics. The training is also useful for those who wish to take the PeopleCert AIOps Foundation international exam later on. The exam is optional.

Module 1: AIOps Foundation

• Historical overview, background
• Introduction, meaning and interpretation of AIOps
• Introduction to main technologies and basic concepts
• Stages in the AIOps system
• Related exercises

Module 2: AIOPs in the organisation

• Promoting AIOps and the factors influencing it
• The relationship between AIOps and DevOps
• The relationship between AIOps and Site Reliability
• AIOps and Security
• Data, Telemetry and Complex Systems
• A new paradigm for understanding system state

Module 3: Key technologies: data

• What is "Big Data"?
• The 5 "V's" of Big Data
• What are the 5 "V's" of Big Data?
• Data sources and types of AIOps
• Data diversity

Module 4: Key technologies: machine learning (ML)

• Artificial Intelligence and Machine Learning
• Supervised and unsupervised machine learning
• Machine learning and analysis
• Machine learning and learning models
• AIOps and the future of AI

Module 5: AIOps and Operational Metrics

• Metrics and Operations
• The most important metrics for overall system monitoring
• Agreements, targets and indicators
• Incident-related metrics
• Incident quantification (MTTD, MTBF, MTTA, MTTR)
• Service level agreement

Module 6: AIOps use cases and organisational mindset

• Shift from reactive to proactive
• Approaching a reactive approach to operations
• Diving into the world of use cases
• Leveling up and AIOps in the organization
• Interpreting the past and anticipating the future

Module 7: Assessing the impact of AIOps

• AIOps and operational metrics
• AIOps, DevOps and SRE
• Improving the accuracy of AI
• AIOps system visualisation
• Monitoring the impact of AIOps
• Impact of Incident-related metrics
• AIOps and DORA metrics

Module 8: Implementing AIOps in the organisation

• Avoiding common pitfalls
• The problem of ethics and machine learning
• Steps of implementation
• Data quality and processing
• Culture and organisational skills
• Data and regulation
• Data protection and user data

No specific prior training is required, but knowledge of basic IT terminology and work experience in IT and IT service management will greatly facilitate understanding of the areas listed in the topics. The training is in Hungarian, but the course materials and, if you wish to sit the exam, the exam are in English; therefore, a basic level of English at document reading level is recommended.