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Accelerating DevSecOps on AWS

You're reading from   Accelerating DevSecOps on AWS Create secure CI/CD pipelines using Chaos and AIOps

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803248608
Length 520 pages
Edition 1st Edition
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Author (1):
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Nikit Swaraj Nikit Swaraj
Author Profile Icon Nikit Swaraj
Nikit Swaraj
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Table of Contents (15) Chapters Close

Preface 1. Section 1:Basic CI/CD and Policy as Code
2. Chapter 1: CI/CD Using AWS CodeStar FREE CHAPTER 3. Chapter 2: Enforcing Policy as Code on CloudFormation and Terraform 4. Chapter 3: CI/CD Using AWS Proton and an Introduction to AWS CodeGuru 5. Section 2:Chaos Engineering and EKS Clusters
6. Chapter 4: Working with AWS EKS and App Mesh 7. Chapter 5: Securing Private EKS Cluster for Production 8. Chapter 6: Chaos Engineering with AWS Fault Injection Simulator 9. Section 3:DevSecOps and AIOps
10. Chapter 7: Infrastructure Security Automation Using Security Hub and Systems Manager 11. Chapter 8: DevSecOps Using AWS Native Services 12. Chapter 9: DevSecOps Pipeline with AWS Services and Tools Popular Industry-Wide 13. Chapter 10: AIOps with Amazon DevOps Guru and Systems Manager OpsCenter 14. Other Books You May Enjoy

AIOps and how it helps in IT operations

AI is a hot topic everywhere and, unlike previously, lots of people now really understand the meaning of AI and how it is applied in real-life scenarios or use cases. Before jumping right to AIOps, we first need to set the context by understanding AI and ML.

AI is the broadest term and has been around for decades as a research topic. AI allows a computer to perform any task that normally requires human intelligence. ML is a subset of AI that solves specific tasks by learning from patterns and data and making predictions without explicitly being programmed. This phrase of explicitly being programmed is the key factor that translates some huge opportunities to transform how we do IT operations today. ML is one approach to AI, recently popular due to big data and cheap compute through cloud computing. Broadly, there are two types of ML algorithms:

  • Supervised learning algorithms take the raw data that you are inputting, and they also...
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