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 explanation using the MLflow artifact logging API

MLflow has a generic tracking API that can log any artifact: mlflow.log_artifact. However, the examples given in the MLflow documentation usually use scikit-learn and tabular numerical data for training, testing, and explaining. Here, we want to show how to use mlflow.log_artifact for an NLP sentimental DL model to log relevant artifacts, such as Shapley value arrays and Shapley value bar plots. You can check out the Python VS Code notebook, shap_mlflow_log_artifact.py, in this chapter's GitHub repository (https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/blob/main/chapter10/notebooks/shap_mlflow_log_artifact.py) to follow along with the steps:

Make sure you have the prerequisites, including a local full-fledged MLflow server and the conda virtual environment, ready. Follow the instructions in the README.md (https://github.com/PacktPublishing/Practical-Deep-Learning-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

Implementing a SHAP explanation using the MLflow artifact logging API

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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 explanation using the MLflow artifact logging API

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