AIOps Foundation (AIOF)®

Course Description
 
DURATION - 16 Hours
Introduces the history, background, technologies, organizational challenges and strategies towards applying artificial intelligence for IT Operations, or AIOps, a rapidly growing industry driven by the rapidly evolving IT operational environments of cloud native applications. Tailored for those focused on understanding basic concepts, implementations, use cases and benefits.
 
OVERVIEW
This AIOps Foundation course aims to cover the origins of AIOps including the history behind the term, patterns that preceded it and the technology context in which it has evolved. Learners will gain an understanding of the processes of combining big data analytics, machine learning algorithms, automation, and optimization into a single platform.
This course introduces key principles and foundational concepts along with the core technologies of AIOps: big data and machine learning. The course will provide students with an understanding of how and why digital transformation, together with the evolution of machine learning, have brought about the rise of AIOps as an indispensable tool in today’s IT Operational landscape.
Core technologies of machine learning and big data will be discussed, as well as the basic concepts of artificial intelligence, different types of machine learning models that can be implemented, and the relationship between AIOps and MLOps, DevOps and Site Reliability.
This foundation course will also provide the student with a solid understanding of the benefits of implementing AIOps in the organization, including common challenges and key steps in ensuring valuable and successful integration of artificial intelligence in the day to day operations of information technology solutions.
Unique and exciting exercises will be used to apply the concepts covered in the course and sample documents, templates, tools, and techniques will be provided to use after the class.
This course positions learners to successfully complete the AIOps Foundation certification exam.
 
COURSE OBJECTIVES
At the end of the course, the following learning objectives are expected to be achieved:
1.    Clear understanding of the history, origins and current developments of AIOps
2.    Define and comprehend basic concepts and key principles within AIOps
3.    Understand general concepts of big data and artificial intelligence, and how they relate to AIOps
4.    Recognize the relationship between AIOps and MLOps
5.    Understand the effectiveness of AIOps deployment and possible benefits
6.    Understand the changes in mindset, collaboration and skills for AIOps to be applied in the organization
7.    Quantify outcomes of an AIOps implementation leveraging industry standard metrics
8.    Understand usual challenges and opportunities of applying AIOps in the organization
9.    Visualize the challenges, trends and ethical considerations organizations might face while deploying an AIOps initiative
 
AUDIENCE
The target audience for the AIOps Foundation course are professionals including:
Anyone focused on IT Operations
Anyone interested in software in today’s IT landscape
AIOps Architects and Engineers
Business Managers, Stakeholders
Cloud Engineers
Data Engineers and Scientists
DevOps Engineers and Practitioners
IT Directors
IT Managers
IT Security Analysts
IT Team Leaders
Product Owners
Scrum Masters
Software Engineers
Site Reliability Engineers
System Integrators
AIOps Platform and Tool Providers
 
LEARNER MATERIALS
Sixteen (16) hours of instructor-led training and discussion facilitation
Participation in unique exercises designed to apply concepts
Sample documents, templates, tools and techniques
Sample exam
Glossary
Access to additional value-added resources and communities
 
PREREQUISITES
Familiarity with IT terminology and IT related work experience are recommended.
 
CERTIFICATION EXAM
Successfully passing (65%) the 60-minute examination, consisting of 40 multiple-choice questions, leads to the AIOps Foundation certificate. The certification is governed and maintained by DevOps Institute, a member of the PeopleCert Group.
 
COURSE OUTLINE
Course Introduction
Module 1: AIOps Foundation
History and Predecessors
Meaning of AIOps
Differences between AIOps and IT Operations Analytics
Core Technologies and Basic Concepts
Stages of an AIOps System
Overlapping Practices
Module 2: AIOps in the Organization
Drivers and Influences
AIOps and DevOps
AIOps and Site Reliability
AIOps and Security
Data, Telemetry and Systems Complexity
A New Paradigm to Understand System State
Module 3: Core Technologies: Data
What is Big Data?
The Five V’s of Big Data
Characteristics of Big Data
AIOps Data Sources and Types
Diverse Data
Module 4: Core Technologies: Machine Learning (ML)
AI and Machine Learning
Supervised vs Unsupervised
Machine Learning vs Analytics
Machine Learning and Training Models
AIOps and the Future of AI
AIOps vs. Analytics Similarities and Differences
Module 5: AIOPs and Operations Metrics
Metrics and Operations
Key Metrics to Track Across Systems
Agreements, Objectives and Indicators
Incident Related Metrics
Quantifying Incidents (MTTD, MTBF, MTTA, MTTR)
Service Level Agreements
Module 6: AIOps Use Cases and Organizational Mindset
Shifting from Reactive to Proactive
Characteristics of a Reactive Approach to Operations
Deterministic to Probabilistic
Deep Dive Into Use Cases
AIOps and Shifts in the Organization
Understanding the Past and Predicting the Future
Module 7: Evaluating AIOps Impact
AIOps and Operations Metrics
AIOps, DevOps and SRE
Improving AI Accuracy
AIOps System Visibility
Tracking Impact of AIOps
Impact to Incident Related Metrics
AIOps and DORA Metrics
Module 8: Implementing AIOps in the Organization
Avoiding Common Challenges
Ethics and Machine Learning
Paths to Implementation
Data Quality and Processes
Culture and Organizational Practices
Data and Regulation
Machine Learning Bias
Privacy and User Data