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

Technical requirements

All of the code examples for this book can be found at the following GitHub URL: https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow.

You need to have Miniconda (https://docs.conda.io/en/latest/miniconda.html) installed on your development environment. In this chapter, we will walk through the process of installing the PyTorch lightning-flash library (https://github.com/PyTorchLightning/lightning-flash), which can be used to build our first DL model in the Implementing a basic DL sentiment classifier section. Alternatively, you can sign up for a free Databricks Community Edition account at https://community.cloud.databricks.com/login.html and use a GPU cluster and a notebook to carry out the model development described in this book.

In addition to this, if you are a Microsoft Windows user, we recommend that you install WSL2 (https://www.windowscentral.com/how-install-wsl2-windows-10) so that you have a Linux environment to run the command lines that are present in this book.

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

Technical requirements

Authors (1)

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