Hands-on AIOps (häftad)
Fler böcker inom
Häftad (Paperback / softback)
Antal sidor
1st ed.
Bhardwaj, Gaurav
166P87 illus 70 Illustrations, color 17 Illustrations, black and white X 70 illus in color
97 Illustrations, black and white; XXIII, 243 p. 97 illus.
234 x 156 x 14 mm
381 g
Antal komponenter
1 Paperback / softback
Hands-on AIOps (häftad)

Hands-on AIOps

Best Practices Guide to Implementing AIOps

Häftad,  Engelska, 2022-07-21
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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
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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.


Chapter 1: What is AIOPs, Need, and BenefitsChapter 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 -topics1. Impact of IT modernization and digitalization2. Challenges with ITOA3. What is artificial intelligence 4. AIOPs - AI in information technology5. 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 overview2. AIOPs architecture and components3. 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 model2. AIOPs for diverse personas - SRE & DevOps3. AIOPs for application development life cycle4. Aligning Dev and Ops via AIOPs.5. SRE principles and AIOPs6. 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 learning2. Why machine learning is important3. Types of machine learning4. Natural language processing 5. Machine learning algorithmic tradeoff6. 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 systemsRoot 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 baselining2. Deduplication3. Anomaly detection4. ML-driven correlation5. Rule-based correlation6. 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 guardrails4. Teams enablement and engagement 5. Visibility and governance6. 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 AIOPs2. AIOPs holds key To digital business transformation3. Unified Framework - AIOPs with SecOps and DevSecOps.