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

Technical requirements

To complete the experiment in this chapter, we will need the following tools, libraries, and GitHub repositories installed or checked out on our computer:

VS Code: The version we use in this book is August 2021 (that is, version 1.60). We use VS Code for our local code development environment. This is the recommended way for local developments. Please refer to https://code.visualstudio.com/updates/v1_60.
MLflow: Version 1.20.2. In this chapter, in the Setting up MLflow section, we will walk through how to set up MLflow locally or remotely. Please refer to https://github.com/mlflow/mlflow/releases/tag/v1.20.2.
Miniconda: Version 4.10.3. Please refer to https://docs.conda.io/en/latest/miniconda.html.
PyTorch lightning-flash: 0.5.0. Please refer to https://github.com/PyTorchLightning/lightning-flash/releases/tag/0.5.0.
The GitHub URL for the code in this chapter: You can find this at https://github.com/PacktPublishing/Practical-Deep-Learning...

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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

Technical requirements

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Table of Contents (17) Chapters

Technical requirements

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