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 3 – Running Deep Learning Pipelines at Scale

In this section, we will learn how to run deep learning (DL) pipelines in different execution environments and perform hyperparameter tuning, or hyperparameter optimization (HPO), at scale. We will start with an overview of the scenarios and requirements for executing DL pipelines in different environments. We will then learn how to use MLflow's command-line interface (CLI) to run in four different execution scenarios in a distributed environment. From there on, we will learn how to choose the best HPO framework by comparing Ray Tune, Optuna, and HyperOpt for tuning hyperparameters of a DL pipeline. Finally, we will concentrate on how to implement and run HPO for DL at scale using state-of-the-art HPO frameworks such as Ray Tune and MLflow.

This section comprises the following chapters:

Chapter 5, Running DL Pipelines in Different Environments
Chapter 6, Running Hyperparameter Tuning at Scale

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

Section 3 – Running Deep Learning Pipelines 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 3 – Running Deep Learning Pipelines at Scale

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Authors (1)

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Section 3 – Running Deep Learning Pipelines at Scale