Welcome to your hands-on guide to artificial intelligence for IT operations (AIOps). This book
provides in-depth coverage including operations and technical aspects. The fundamentals of
machine learning (ML) and artificial intelligence (AI) that form the core of AIOps are
explained as well as the implementation of multiple AIOps uses cases using ML algorithms. The
book begins with an overview of AIOps covering its relevance and benefits in the current IT
operations landscape. The authors discuss the evolution of AIOps its architecture
technologies AIOps challenges and various practical use cases to efficiently implement AIOps
and continuously improve it. The book provides detailed guidance on the role of AIOps in site
reliability engineering (SRE) and DevOps models and explains how AIOps enables key SRE
principles. The book provides ready-to-use best practices for implementing AIOps in an
enterprise. Each component of AIOps and ML using Python code andtemplates is explained and
shows how ML can be used to deliver AIOps use cases for IT operations.What You Will Learn Know
what AIOps is and the technologies involved Understand AIOps relevance through use cases
Understand AIOps enablement in SRE and DevOps Understand AI and ML technologies and algorithms
Use algorithms to implement AIOps use cases Use best practices and processes to set up AIOps
practices in an enterprise Know the fundamentals of ML and deep learning Study a hands-on use
case on de-duplication in AIOps Use regression techniques for automated baselining Use anomaly
detection techniques in AIOps Who This Book is For AIOps enthusiasts monitoring and management
consultants observability engineers site reliability engineers infrastructure architects
cloud monitoring consultants service management experts DevOps architects DevOps engineers
and DevSecOps experts
