Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
 Architectural Patterns and Techniques for Developing IoT Solutions

You're reading from   Architectural Patterns and Techniques for Developing IoT Solutions Build IoT applications using digital twins, gateways, rule engines, AI/ML integration, and related patterns

Arrow left icon
Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803245492
Length 304 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Jasbir Singh Dhaliwal Jasbir Singh Dhaliwal
Author Profile Icon Jasbir Singh Dhaliwal
Jasbir Singh Dhaliwal
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding IoT Patterns
2. Chapter 1: Introduction to IoT Patterns FREE CHAPTER 3. Chapter 2: IoT Patterns for Field Devices 4. Chapter 3: IoT Patterns for the Central Server 5. Part 2: IoT Patterns in Action
6. Chapter 4: Pattern Implementation in the Consumer Domain 7. Chapter 5: Pattern Implementation in the Smart City Domain 8. Chapter 6: Pattern Implementation in the Retail Domain 9. Chapter 7: Pattern Implementation in the Manufacturing Domain 10. Chapter 8: Pattern Implementation in the Agriculture Domain 11. Part 3: Implementation Considerations
12. Chapter 9: Sensor and Actuator Selection Guidelines 13. Chapter 10: Analytics in the IoT Context 14. Chapter 11: Security in the IoT Context 15. Part 4: Extending IoT Solutions
16. Chapter 12: Exploring Synergies with Emerging Technologies 17. Chapter 13: Epilogue 18. Index 19. Other Books You May Enjoy

Generative AI

Generative AI has received considerable attention in the recent past. It is a form of AI that can generate unique content (images, video, audio, tests, and even code or logic) based on the provided keywords, where the content’s quality closely mimics the content a human would have created.

This aspect of generating unique content differentiates generative AI from traditional AI, which relied primarily on a predefined set of patterns, rules, formulas, and more. Additionally, generative AI is trained on a considerable volume and variety of input data/content (images, sound, video, programming patterns, and more) and it can self-learn or adapt to insert novel/unique elements into the generated content rather than simply combine content from multiple input sources. In addition to being trained on a huge corpus of content, generative AI leverages deep learning techniques to continuously fine-tune parameters, resulting in more and more authentic output with time.

...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image