Hands–on AIOps Best Practices Guide to Implementing AIOps

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 and templates 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 involvedUnderstand AIOps relevance through use casesUnderstand AIOps enablement in SRE and DevOpsUnderstand AI and ML technologies and algorithmsUse algorithms to implement AIOps use casesUse best practices and processes to set up AIOps practices in an enterpriseKnow the fundamentals of ML and deep learningStudy a hands-on use case on de-duplication in AIOpsUse regression techniques for automated baseliningUse 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