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The Deep Learning Architect's Handbook

You're reading from  The Deep Learning Architect's Handbook

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Pages 516 pages
Edition 1st Edition
Languages
Author (1):
Ee Kin Chin Ee Kin Chin
Profile icon Ee Kin Chin

Table of Contents (25) Chapters

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

Discovering general recommendations for DL deployment

Here, we will discover DL deployment recommendations related to three verticals, namely model safety, trust, and reliability assurance, model latency optimization, and tools that help abstract model deployment-related decisions and ease the model deployment process. We will dive into the three verticals one by one.

Model safety, trust, and reliability assurance

Ensuring model safety, trust, and reliability is a crucial aspect of deploying DL systems. In this section, we will explore various recommendations and best practices to help you establish a robust framework for maintaining the integrity of your models. This includes compliance with regulations, implementing guardrails, prediction consistency, comprehensive testing, staging and production deployment strategies, usability tests, retraining and updating deployed models, human-in-the-loop decision-making, and model governance. By adopting these measures, you can effectively...

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