Hands-on AIOps: Best Practices Guide to Implementing AIOps

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