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Enterprise DevOps for Architects

You're reading from   Enterprise DevOps for Architects Leverage AIOps and DevSecOps for secure digital transformation

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801812153
Length 288 pages
Edition 1st Edition
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Authors (2):
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Jeroen Mulder Jeroen Mulder
Author Profile Icon Jeroen Mulder
Jeroen Mulder
Jeroen Mulder Jeroen Mulder
Author Profile Icon Jeroen Mulder
Jeroen Mulder
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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Architecting DevOps for Enterprises
2. Chapter 1: Defining the Reference Architecture for Enterprise DevOps FREE CHAPTER 3. Chapter 2: Managing DevOps from Architecture 4. Chapter 3: Architecting for DevOps Quality 5. Chapter 4: Scaling DevOps 6. Chapter 5: Architecting Next-Level DevOps with SRE 7. Section 2: Creating the Shift Left with AIOps
8. Chapter 6: Defining Operations in Architecture 9. Chapter 7: Understanding the Impact of AI on DevOps 10. Chapter 8: Architecting AIOps 11. Chapter 9: Integrating AIOps in DevOps 12. Chapter 10: Making the Final Step to NoOps 13. Section 3: Bridging Security with DevSecOps
14. Chapter 11: Understanding Security in DevOps 15. Chapter 12: Architecting for DevSecOps 16. Chapter 13: Working with DevSecOps Using Industry Security Frameworks 17. Chapter 14: Integrating DevSecOps with DevOps 18. Chapter 15: Implementing Zero Trust Architecture 19. Assessments 20. Other Books You May Enjoy

Assessing the enterprise readiness of AI-enabled DevOps

So far, we've learned that digital transformation is a process. It doesn't come in one go; the enterprise needs to be prepared for this. It includes adopting cloud platforms and cloud-native technology. Enterprises will have legacy systems and likely a lot of data sitting in different silos, leaving the enterprise with the challenge that this data is used in an optimized way. It's a misperception to think that AI-enabled tools and data science can solve this issue from the beginning.

The enterprise will need to have a complete overview of all its assets, but also its skills and capabilities. First, data specialists will need to assess the locations, formats, and usability of data sources. The data scientists then will have to design data models. They can't do this in isolation: they will have to collaborate with DevOps engineers and the application owners to agree on things such as version control, model...

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