Hands-On Aiops: Best Practices Guide to Implementing Aiops

ISBN-13: 9781484282663 / Angielski / Miękka / 2022 / 243 str.

Gaurav Bhardwaj

Opis

Intermediate user level

Spis treści

Chapter 1: What is AIOPs, Need, and Benefits

Chapter goal: This chapter talks about challenges that IT modernization and business digitalization are posing to IT business and how AIOPs can help in overcoming them as well as sustain and stay relevant in this post-pandemic economy.

No of pages 15

Sub -topics

1. Impact of IT modernization and digitalization

2. Challenges with ITOA

3. What is artificial intelligence 

4. AIOPs - AI in information technology

5. AIOPs businesses levers

               

Chapter 2: AIOPs Architecture, Methodology, Challenges

Chapter goal: Explain technologies and components involved in AIOPs architecture along with its implementation methodology and challenges.

No of pages: 12

Sub - Topics 

1. AIOPs overview

2. AIOPs architecture and components

3. AIOPs implementation methodology 

4. AIOPs challenges

 

 

Chapter 3: AIOPs Supporting Site Reliability Engineering and DevOps

Chapter goal: Explain the use of AIOPs in SRE in keeping services up and running and the DevOps process of product development to operations.

No of pages: 15

 

Sub - Topics:  

1. Overview of SRE and DevOps model

2. AIOPs for diverse personas - SRE & DevOps

3. AIOPs for application development life cycle

4. Aligning Dev and Ops via AIOPs.

5. SRE principles and AIOPs

6. AIOPs enabling visibility in SRE and DevOps

 

 

Chapter 4: Fundamentals of Machine learning and AI

Chapter Goal: Explain the technology and concepts behind artificial intelligence and machine learning.

No of pages: 12

Sub - Topics: 

1. What is machine learning

2. Why machine learning is important

3. Types of machine learning

4. Natural language processing 

5. Machine learning algorithmic tradeoff

6. Principles of artificial intelligence

 

Chapter 5: AIOPs Use Cases

Chapter Goal: Explain practical scenarios or tasks which can be facilitated by AIOPs.

No of pages: 8

Sub - Topics:

Monitoring of software systems

Root cause analysis with AIOps 

Security use cases 

Chapter 6: Applying Machine learning for AIOPS: 

Chapter Goal: Provide No. of pages: 12

Sub - Topics:

1. Automated baselining

2. Deduplication

3. Anomaly detection

4. ML-driven correlation

5. Rule-based correlation

6. AIOPs in detect-to-correct value chain

 

Chapter 7: Setting up of AIOPs 

Chapter Goal: Provide best practices for AIOPs journey and guidance on setting up of AIOPs practic.

No. of pages: 10

Sub - Topics:

1. AIOPs implementation framework.

2. Define roadmap of AIOPs 

3. Setting up guardrails

4. Teams enablement and engagement 

5. Visibility and governance

6. Continous improvement

Chapter 8: Future of AIOPs

Chapter Goal: Provide a blueprint of AIOPs future and its impact on the IT industry.

No. of pages: 3

Sub - Topics:

1. Transition from domain-centric to domain-agnostic AIOPs

2. AIOPs holds key To digital business transformation

3. Unified Framework - AIOPs with SecOps and DevSecOps.

O autorze

Navin Sabharwal is currently Chief Architect and Head of Strategy for Autonomics, named "DRYiCE" at HCL technologies. He is responsible for innovation, presales, and delivery of award-winning autonomics platforms for HCL technologies. Navin is an innovator, thought leader, author, and a consultant in areas of AI and machine learning (ML), observability, AIOps, DevOps, DevSecOps, engineering, and R&D. He is responsible for IP development & service delivery in the areas of AI and ML, automation products, cloud computing, public cloud AWS, Microsoft Azure, VMWare private cloud, Microsoft private cloud, data center automation, analytics for IT operations, and IT service management.

Gaurav Bhardwaj is a seasoned IT professional and technology evangelist with expertise in service assurance, cloud computing, AI/ML-based software product development, engineering, and data analytics. He has international experience in developing and executing IT automation strategies and solutions that are aligned with business goals as well as heading multi-million USD services globally. Gaurav has a proven track record of achievements in roles of enterprise architect and consultant for large and complex global engagements (includes multiple Fortune 500 companies) leveraging automation as the pivot for business development as well as for transforming legacy environments/platforms into next-generation IT environments powered by cloud-native and containerized apps, SDI, and AIOps and DevOps methodologies.

Blurb

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