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Practical Deep Learning at Scale with MLflow

You're reading from Practical Deep Learning at Scale with MLflow

Product type Book

Published in Jul 2022

Publisher Packt

ISBN-13 9781803241333

Pages 288 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Yong Liu

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

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 understand the examples in this chapter, the following key technical requirements are needed:

Ray Tune 1.9.2: This is a flexible and powerful hyperparameter tuning framework (https://docs.ray.io/en/latest/tune/index.html).
Optuna 2.10.0: This is an imperative and define-by-run hyperparameter tuning Python package (https://optuna.org/).
The code for this chapter can be found in the following GitHub URL, which also includes the requirements.txt file that contains the preceding key packages and other dependencies: https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/tree/main/chapter06.

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Authors (1)

Yong Liu

Yong Liu has been working in big data science, machine learning, and optimization since his doctoral student years at the University of Illinois at Urbana-Champaign (UIUC) and later as a senior research scientist and principal investigator at the National Center for Supercomputing Applications (NCSA), where he led data science R&D projects funded by the National Science Foundation and Microsoft Research. He then joined Microsoft and AI/ML start-ups in the industry. He has shipped ML and DL models to production and has been a speaker at the Spark/Data+AI summit and NLP summit. He has recently published peer-reviewed papers on deep learning, linked data, and knowledge-infused learning at various ACM/IEEE conferences and journals.

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