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Hybrid Cloud Infrastructure and Operations Explained

You're reading from   Hybrid Cloud Infrastructure and Operations Explained Accelerate your application migration and modernization journey on the cloud with IBM and Red Hat

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803248318
Length 344 pages
Edition 1st Edition
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Author (1):
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Mansura Habiba Mansura Habiba
Author Profile Icon Mansura Habiba
Mansura Habiba
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Moving to Hybrid Cloud
2. Chapter 1: An Introduction to Hybrid Cloud Modernization FREE CHAPTER 3. Chapter 2: Understanding Cloud Modernization and and Innovation Fundamentals 4. Chapter 3: xploring Best Practices for the Cloud Journey 5. Part 2: Cloud-Native Methods, Practices, and Technology
6. Chapter 4: Developing Applications in a Cloud Native Way 7. Chapter 5: Exploring Application Modernization Essentials 8. Part 3: Elements of Embedded Linux
9. Chapter 6: Designing and Implementing Cloud Storage Services 10. Chapter 7: Designing and Implementing Networking in Hybrid Cloud Infrastructure 11. Chapter 8: Understanding Security in Action 12. Chapter 9: Designing a Resilient Platform for Cloud Migration 13. Chapter 10: Managing Operations in Hybrid Cloud Infrastructure 14. Other Books You May Enjoy Appendix A –Application Modernization and Migration Checklist

Understanding reference architectures for MLOPs

Machine learning jobs are now an essential part of any software and organization. Moreover, machine learning and data analytics components are both dynamic and continuous. Therefore, Machine Learning Operations (MLOps) is crucial for organizations. Figure 10.6 shows the reference architecture for MLOps. This section will discuss how IBM Cloud Pak for Data can provide an end-to-end MLOps solution.

Project governance

MLOps mainly needs project governance, security operations such as identity and access control management, and data and network security operations. MLOps starts with project governance, where organizations can manage multiple data analytics and insight projects. Each project has its assets, such as data, Jupyter notebooks, models, people, dashboards, data flows, and more:

Figure 10.6 – Reference architecture for MLOps

Figure 10.7 shows different assets for a data science project in IBM...

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