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

View More author details

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

Exploring the SHAP Explainability toolbox

For our learning purpose, let's review some popular explainability toolboxes while experimenting with some examples. Based on the number of GitHub stars (16,000 as of April 2022, https://github.com/slundberg/shap), SHAP is the most widely used and integrated open source model explainability toolbox. It is also the foundation explanation tool that is integrated with MLflow. Here, we would like to run a small experiment to get some hands-on experience on how this works. Let's use a sentimental analysis NLP model to explore how SHAP can be used for explaining the model behavior:

Set up the virtual environment on your local environment after checking out this chapter's code from GitHub. Running the following command will create a new virtual environment called dl-explain:
```
conda env create -f conda.yaml
```

This will install SHAP and its related dependencies such as matplotlib in this virtual environment. Once this virtual...

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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

Exploring the SHAP Explainability toolbox

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

Exploring the SHAP Explainability toolbox

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