AIOps Foundation – Accredited Training

Prerequisites:

Basic understanding of IT terminology and experience working with information technologies.

Overview:

AIOps is a rapidly evolving field that addresses the needs of modern, complex IT environments—particularly those operating within cloud architectures. The AIOps Foundation course offers a comprehensive introduction to the concepts, technologies, and practices related to the use of artificial intelligence in IT operations.

The program covers the background of AIOps, its core principles, tools, and the organizational challenges faced by IT teams adopting these approaches.

The training concludes with an exam. Passing it grants the globally recognized AIOps Foundation certification, valid for three years.

Who is it for?

This course is designed for professionals and managers involved in:

IT operations

DevOps and Site Reliability Engineering (SRE)

Cloud architecture

Data analysis and Data Science

Software development

IT security

Product and project management

Course Outline:

Introduction to AIOps

Origins and evolution of AIOps

The importance of AIOps in modern IT

AIOps vs. IT Operations Analytics – key differences

Core technologies and concepts

AIOps system lifecycle

Related practices and methodologies

AIOps in the Organizational Context

Key drivers and influencing factors

Integration with DevOps

The role of AIOps in Site Reliability Engineering (SRE)

AIOps and IT security concerns

Data, telemetry, and system complexity

A new paradigm for understanding system health

Core Technologies – Data

What is Big Data?

The 5 Vs of Big Data

Characteristics of Big Data in AIOps

Data sources and types in AIOps environments

Data diversity and processing challenges

Core Technologies – Machine Learning (ML)

AI, ML, and their role in AIOps

Supervised vs. unsupervised learning in AIOps

Machine learning vs. traditional analytics

ML models and their application in AIOps

The future of AI in IT operations

Comparing ML with data analytics approaches

AIOps and Operational Metrics

Key operational metrics for IT environments

Important indicators across various systems

SLA, SLO, and KPI – definitions and usage

Incident-related metrics: detection and classification

Time-based metrics: MTTD, MTBF, MTTA, MTTR

Managing service level agreements

Use Cases and Organizational Mindset Shift

From reactive to proactive operations

Characteristics of a reactive IT operations model

Moving from deterministic to probabilistic approaches

Real-world use cases of AIOps

Organizational change driven by AIOps

Understanding the past, predicting the future

Measuring the Impact of AIOps

Key AIOps metrics for IT operations

Synergy between AIOps, DevOps, and SRE

Improving AI accuracy through AIOps

Enhancing system observability

Tracking AIOps impact on operations

Connecting AIOps metrics with DORA indicators

Implementing AIOps in the Organization

Avoiding common pitfalls

Ethics and machine learning in AIOps

Implementation paths and strategies

Data quality and process alignment

Organizational culture and supporting practices

Data regulations and compliance

Handling ML model errors

Privacy and user data protection