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Solutions Architect's Handbook

You're reading from   Solutions Architect's Handbook Kick-start your career with architecture design principles, strategies, and generative AI techniques

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835084236
Length 578 pages
Edition 3rd Edition
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Authors (2):
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Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
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Toc

Table of Contents (20) Chapters Close

Preface 1. Solutions Architects in Organizations FREE CHAPTER 2. Principles of Solution Architecture Design 3. Cloud Migration and Cloud Architecture Design 4. Solution Architecture Design Patterns 5. Cloud-Native Architecture Design Patterns 6. Performance Considerations 7. Security Considerations 8. Architectural Reliability Considerations 9. Operational Excellence Considerations 10. Cost Considerations 11. DevOps and Solution Architecture Framework 12. Data Engineering for Solution Architecture 13. Machine Learning Architecture 14. Generative AI Architecture 15. Rearchitecting Legacy Systems 16. Solution Architecture Document 17. Learning Soft Skills to Become a Better Solutions Architect 18. Other Books You May Enjoy
19. 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 the company’s direction. It’s not just about setting up the business vision but also improving the end user experience by predicting and fulfilling their needs or automating day-to-day decision-making activities such as loan approval.

However, with the sheer amount of data available now, it’s become difficult for the human brain to process all data and predict the future. That’s where artificial intelligence (AI) and machine learning (ML) come in. AI is the broader concept of machines carrying out tasks in smart ways like Siri and Alexa to understand your questions and give answers, and ML is a specific subset of AI that involves teaching...

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