Hands-on AIOps : best practices guide to implementing AIOps

Hands-on AIOps best practices guide to implementing AIOps

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 i...

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Detalles Bibliográficos
Otros Autores: Sabharwal, Navin, author (author), Bhardwaj, Gaurav, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: New York, New York : Apress L. P. [2022]
Materias:
Computer software > Development.
Artificial intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009671496506719
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Acknowledgments
  • Preface
  • Introduction
  • Chapter 1: What Is AIOps?
  • Introduction to AIOps
  • Data Ingestion Layer
  • Data Processing Layer
  • Data Representation Layer
  • AIOps Benefits
  • Summary
  • Chapter 2: AIOps Architecture and Methodology
  • AIOps Architecture
  • The Core Platform
  • Big Data
  • Volume
  • Velocity
  • Variety
  • Veracity
  • Value
  • Machine Learning
  • The Three Key Areas in AIOps
  • Observe
  • Data Ingestion
  • Integration
  • Event Suppression
  • Event Deduplication
  • Rule-Based Correlation
  • Machine Learning-Based Correlation
  • Anomaly Detection
  • Event Correlation
  • Root-Cause Analysis
  • Predictive Analysis
  • Visualization
  • Collaboration
  • Feedback
  • Engage
  • Incident Creation
  • Task Assignment
  • Task Analytics
  • Agent Analytics
  • Change Analytics
  • Process Analytics
  • Visualization
  • Collaboration
  • Feedback
  • Act
  • Automation Recommendation
  • Automation Execution
  • Incident Resolution
  • SR Fulfilment
  • Change Orchestration
  • Automation Analytics
  • Visualization
  • Collaboration
  • Feedback
  • Application Discovery and Insights
  • Making Connections: The Value of Data Correlation
  • Summary
  • Chapter 3: AIOps Challenges
  • Organizational Change Management
  • Monitoring Coverage and Data Availability
  • Rigid Processes
  • Lack of Understanding of Machine Learning and AIOps
  • Expectations Mismatch
  • Fragmented Functions and the CMDB
  • Challenges in Machine Learning
  • Data Drift
  • Predictive Analytics Challenges
  • Cost Savings Expectations
  • Lack of Domain Inputs
  • Summary
  • Chapter 4: AIOps Supporting SRE and DevOps
  • Overview of SRE and DevOps
  • SRE Principles and AIOps
  • Principle 1: Embracing Risk
  • Principle 2: Service Level Objectives
  • Principle 3: Eliminating Toil.
  • Principle 4: Monitoring
  • Principle 5: Automation
  • Principle 6: Release Engineering
  • Principle 7: Simplicity
  • AIOps Enabling Visibility in SRE and DevOps
  • Culture
  • Automation of Processes
  • Measurement of Key Performance Indicators (KPIs)
  • Sharing
  • Summary
  • Chapter 5: Fundamentals of Machine Learning and AI
  • What Is Artificial Intelligence and Machine Learning?
  • Why Machine Learning Is Important
  • Types of Machine Learning
  • Machine Learning
  • Supervised (Inductive) Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Differences Between Supervised and Unsupervised Learning
  • Choosing the Machine Learning Approach
  • Natural Language Processing
  • What Is Natural Language Processing?
  • Syntactic Analysis
  • Semantic Analysis
  • NLP AIOps Use Cases
  • Sentiment Analysis
  • Language Translation
  • Text Extraction
  • Topic Classification
  • Deep Learning
  • Summary
  • Chapter 6: AIOps Use Case: Deduplication
  • Environment Setup
  • Software Installation
  • Launch Application
  • Performance Analysis of Models
  • Mean Square Error/Root Mean Square Error
  • Mean Absolute Error
  • Mean Absolute Percentage Error
  • Root Mean Squared Log Error
  • Coefficient of Determination-R2 Score
  • Deduplication
  • Summary
  • Chapter 7: AIOps Use Case: Automated Baselining
  • Automated Baselining Overview
  • Regression
  • Linear Regression
  • Time-Series Models
  • Time-Series Data
  • Stationary Time Series
  • Lag Variable
  • ACF and PACF
  • ARIMA
  • Model Development
  • Differencing (d)
  • Autoregression or AR (p)
  • Moving Average or MA (q)
  • SARIMA
  • Implementation of ARIMA and SARIMA
  • Automated Baselining in APM and SecOps
  • Challenges with Dynamic Thresholding
  • Summary
  • Chapter 8: AIOps Use Case: Anomaly Detection
  • Anomaly Detection Overview
  • K-Means Algorithms
  • Correlation and Association.
  • Topology-Based Correlation
  • Network Topology Correlation
  • Application Topology Correlation
  • Summary
  • Chapter 9: Setting Up AIOps
  • Step 1: Write an AIOps Charter
  • Step 2: Build Your AIOps Team
  • Step 3: Define Your AIOps Landscape
  • Step 4: Define Integrations and Data Sources
  • Step 5: Install and Configure the AIOps Engine
  • Step 6: Configure AIOps Features
  • Step 7: Deploy the Service Management Features
  • Step 8: Deploy Automation Features
  • Step 9: Measure Success
  • Step 10: Celebrate and Share Success
  • Guidelines on Implementing AIOps
  • Hype vs. Clarity
  • Be Goal and KPI Driven
  • Expectations
  • Time to Realize Benefits
  • One Size Doesn't Fit All
  • Organizational Change Management
  • Plan Big, Start Small, and Iterate Fast
  • Continually Improve
  • The Future of AIOps
  • Summary
  • Index.

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