Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Deep Learning Architect's Handbook

You're reading from   The Deep Learning Architect's Handbook Build and deploy production-ready DL solutions leveraging the latest Python techniques

Arrow left icon
Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Length 516 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ee Kin Chin Ee Kin Chin
Author Profile Icon Ee Kin Chin
Ee Kin Chin
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1 – Foundational Methods
2. Chapter 1: Deep Learning Life Cycle FREE CHAPTER 3. Chapter 2: Designing Deep Learning Architectures 4. Chapter 3: Understanding Convolutional Neural Networks 5. Chapter 4: Understanding Recurrent Neural Networks 6. Chapter 5: Understanding Autoencoders 7. Chapter 6: Understanding Neural Network Transformers 8. Chapter 7: Deep Neural Architecture Search 9. Chapter 8: Exploring Supervised Deep Learning 10. Chapter 9: Exploring Unsupervised Deep Learning 11. Part 2 – Multimodal Model Insights
12. Chapter 10: Exploring Model Evaluation Methods 13. Chapter 11: Explaining Neural Network Predictions 14. Chapter 12: Interpreting Neural Networks 15. Chapter 13: Exploring Bias and Fairness 16. Chapter 14: Analyzing Adversarial Performance 17. Part 3 – DLOps
18. Chapter 15: Deploying Deep Learning Models to Production 19. Chapter 16: Governing Deep Learning Models 20. Chapter 17: Managing Drift Effectively in a Dynamic Environment 21. Chapter 18: Exploring the DataRobot AI Platform 22. Chapter 19: Architecting LLM Solutions 23. Index 24. Other Books You May Enjoy

Identifying key DL model deployment requirements

To determine the most suitable deployment strategy from a variety of options, it is essential to identify and define seven key requirements. These are latency and availability, cost, scalability, model hardware, data privacy, safety, and trust and reliability requirements. Let’s dive into each of these requirements in detail:

  • Latency and availability requirements: These are two closely connected components and should be defined together. Availability requirements refer to the desired level of uptime and accessibility of the model’s prediction. Latency requirements refer to the maximum acceptable delay or response time that the models must meet to provide timely predictions or results. A deployment with a low availability requirement usually can tolerate high latency predictions, and vice versa. One reason is that a low-latency capable infrastructure can’t ensure low latency if it is not available when model...
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 €18.99/month. Cancel anytime