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Solutions Architect's Handbook - Second Edition

You're reading from  Solutions Architect's Handbook - Second Edition

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801816618
Pages 590 pages
Edition 2nd Edition
Languages
Authors (2):
Saurabh Shrivastava Saurabh Shrivastava
Profile icon Saurabh Shrivastava
Neelanjali Srivastav Neelanjali Srivastav
Profile icon Neelanjali Srivastav
View More author details

Table of Contents (22) Chapters

Preface 1. The Meaning of Solution Architecture 2. Solution Architects in an Organization 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

Machine Learning Architecture

In the previous chapter, you learned about ingesting and processing big data and getting insights to understand your business. In the traditional way of running a business, the organization's decision-maker looks at past data and uses their experience to plot the future course of company direction. It's not just about setting up the business vision but also improving the end-user experience by predicting their needs and delighting them or automating day-to-day decision-making activities such as loan approval.

However, with the sheer amount of data availability, now, it's become difficult for the human brain to process all data and predict the future. That's where machine learning (ML) helps us predict future courses of action by looking at a large amount of historical data. Most enterprises are either investing in ML today or planning to do so. It is fast becoming the technology that helps companies differentiate themselves—...

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