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

Running locally with local code

Let's start with the first running scenario using the same Natural Language Processing (NLP) text sentiment classification example as the driving use case. You are advised to check out the following version of the source code from the GitHub location to follow along with the steps and learnings: https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/tree/26119e984e52dadd04b99e6f7e95f8dda8b59238/chapter05. Note that this requires a specific Git hash committed version, as shown in the URL path. That means we are asking you to check out a specific committed version, not the main branch.

Let's start with the DL pipeline that downloads the review data to local storage as a first execution exercise. Once you check out this chapter's code, you can type the following command line to execute the DL pipeline's first step:

mlflow run . --experiment-name='dl_model_chapter05' -P pipeline_steps='download_data...

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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

Running locally with local code

Authors (1)

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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

Running locally with local code

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Authors (1)

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