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Solutions Architect's Handbook

You're reading from   Solutions Architect's Handbook Kick-start your career as a solutions architect by learning architecture design principles and strategies

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801816618
Length 590 pages
Edition 2nd Edition
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Authors (2):
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Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
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Toc

Table of Contents (22) Chapters Close

Preface 1. The Meaning of Solution Architecture 2. Solution Architects in an Organization FREE CHAPTER 3. Attributes of the Solution Architecture 4. Principles of Solution Architecture Design 5. Cloud Migration and Hybrid Cloud Architecture Design 6. Solution Architecture Design Patterns 7. Performance Considerations 8. Security Considerations 9. Architectural Reliability Considerations 10. Operational Excellence Considerations 11. Cost Considerations 12. DevOps and Solution Architecture Framework 13. Data Engineering for Solution Architecture 14. Machine Learning Architecture 15. The Internet of Things Architecture 16. Quantum Computing 17. Rearchitecting Legacy Systems 18. Solution Architecture Document 19. Learning Soft Skills to Become a Better Solution Architect 20. Other Books You May Enjoy
21. Index

What is machine learning?

ML drives better customer experiences, more efficient business operations, and faster, more accurate decision making. With the rise in compute power and the proliferation of data, ML has moved from the periphery to be a core differentiator for businesses and organizations across industries. ML use cases can apply to most businesses, like personalized product and content recommendations, contact center intelligence, virtual identity verification, and intelligent document processing. And there are customized use cases built for a specific industry—like clinical trials in pharma or assembly line quality control in manufacturing.

Let's say your company wants to send marketing offers to potential customers for a new toy launch, and you have been tasked to develop a system to identify who to target for the marketing campaign. Your customer base could be millions of users to which you need to apply predictive analytics, and ML can help you solve...

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