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 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 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
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 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 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
