You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781803241333

Length 288 pages

Edition 1st Edition

Tools

MLflow

Concepts

Deep Learning

Author (1):

Yong Liu

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Table of Contents (17) Chapters

Preface

1. Section 1 - Deep Learning Challenges and MLflow Prime

2. Chapter 1: Deep Learning Life Cycle and MLOps Challenges FREE CHAPTER

3. Chapter 2: Getting Started with MLflow for Deep Learning

4. Section 2 – Tracking a Deep Learning Pipeline at Scale

5. Chapter 3: Tracking Models, Parameters, and Metrics

6. Chapter 4: Tracking Code and Data Versioning

7. Section 3 – Running Deep Learning Pipelines at Scale

8. Chapter 5: Running DL Pipelines in Different Environments

9. Chapter 6: Running Hyperparameter Tuning at Scale

10. Section 4 – Deploying a Deep Learning Pipeline at Scale

11. Chapter 7: Multi-Step Deep Learning Inference Pipeline

12. Chapter 8: Deploying a DL Inference Pipeline at Scale

13. Section 5 – Deep Learning Model Explainability at Scale

14. Chapter 9: Fundamentals of Deep Learning Explainability

15. Chapter 10: Implementing DL Explainability with MLflow

16. Other Books You May Enjoy

Understanding the categories and audience of explainability

As this chapter's opening texts imply, explainability for a DL system becomes increasingly critical, sometimes even mandatory, in highly regulated industries such as financial, legal, governmental, and medical application domains. An example lawsuit partially due to the lack of ML explainability is the case of B2C2 v Quoine (https://www.scl.org/articles/12130-explainable-machine-learning-how-can-you-determine-what-a-party-knew-or-intended-when-a-decision-was-made-by-machine-learning), where automated AI trading algorithms mistakenly placed an order with 250 times the market price for bitcoin trading. The recent successful applications of DL models in production stimulate active and abundant research and development in the explainability area due to the need to understand why and how a DL model works. You may have heard of the term explainable artificial intelligence (XAI), which was started by the US Defense Advanced...

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You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Table of Contents (17) Chapters

Understanding the categories and audience of explainability

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You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Table of Contents (17) Chapters

Understanding the categories and audience of explainability

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