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

Section 5 – Deep Learning Model Explainability at Scale

In this section, we will learn about the foundational concepts of explainability and explainable artificial intelligence (XAI) and how to implement deep learning (DL) explainability with MLflow. We will start with an overview of the eight dimensions of explainability and then learn how to use SHapley Additive exPlanations (SHAP) and Transformers Interpret to perform explainability for a natural language processing (NLP) pipeline. Furthermore, we will learn and analyze the current MLflow integration with SHAP to understand the trade-offs and avoid potential implementation problems. Then, we will show how to implement SHAP using MLflow's logging APIs. Finally, we will learn how to implement a SHAP explainer as an MLflow Python model and then load it as either a Spark UDF for batch explanation or as a web service for online Explanation-as-a-Service (EaaS).

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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

Section 5 – Deep Learning Model Explainability at Scale

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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

Section 5 – Deep Learning Model Explainability at Scale

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