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

Implementing a SHAP explainer using the MLflow pyfunc API

As we know from the previous section, a SHAP explainer can be used offline whenever needed by creating a new instance of an explainer using SHAP APIs. However, as the underlying DL models are often logged into the MLflow server, it is desirable to also log the corresponding explainer into the MLflow server, so that we not only keep track of the DL models, but also their explainers. In addition, we can use the generic MLflow pyfunc model logging and loading APIs for the explainer, thus unifying access to DL models and their explainers.

In this section, we will learn step-by-step how to implement a SHAP explainer as a generic MLflow pyfunc model and how to use it for offline and online explanation. We will break the process up into three subsections:

Creating and logging an MLflow pyfunc explainer
Deploying an MLflow pyfunc explainer for an EaaS
Using an MLflow pyfunc explainer for batching explanation

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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

Implementing a SHAP explainer using the MLflow pyfunc API

Authors (1)

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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

Implementing a SHAP explainer using the MLflow pyfunc API

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