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

Understanding automatic HPO for DL pipelines

Automatic HPO has been studied for over two decades since the first known paper on this topic was published in 1995 (https://www.sciencedirect.com/science/article/pii/B9781558603776500451). It has been widely understood that tuning hyperparameters for an ML model can improve the performance of the model – sometimes, dramatically. The rise of DL models in recent years has triggered a new wave of innovation and the development of new frameworks to tackle HPO for DL pipelines. This is because a DL model pipeline imposes many new and large-scale optimization challenges that cannot be easily solved by previous HPO methods. Note that, in contrast to the model parameters that can be learned during the model training process, a set of hyperparameters must be set before training.

Difference between HPO and Transfer Learning's Fine-Tuning

In this book, we have been focusing on one successful DL approach called Transfer Learning...

The rest of the chapter is locked

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 automatic HPO for DL pipelines

Authors (1)

Personalised recommendations for you

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 automatic HPO for DL pipelines

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you