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 current MLflow explainability integration

MLflow has several ways to support explainability integration. When implementing explainability, we refer to two types of artifacts: explainers and explanations:

An explainer is an explainability model, and a common one is a SHAP model that could be different kinds of SHAP explainers, such as TreeExplainer, KernelExplainer, and PartitionExplainer (https://shap.readthedocs.io/en/latest/generated/shap.explainers.Partition.html). For computational efficiency, we usually choose PartitionExplainer for DL models.
An explanation is an artifact that shows some form of output from the explainer, which could be text, numerical values, or plots. Explanations can happen in offline training or testing, or can happen during online production. Thus, we should be able to provide an explainer for offline evaluation or an explainer endpoint for online queries if we want to know why the model provides certain predictions.

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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 current MLflow explainability integration

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Table of Contents (17) Chapters

Understanding current MLflow explainability integration

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