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

Summary

In this chapter, we covered the fundamentals and challenges of HPO, why it is important for the DL model pipeline, and what a modern HPO framework should support. We compared three popular frameworks – Ray Tune, Optuna, and HyperOpt – and picked Ray Tune as the winner for running state-of-the-art HPO at scale. We saw how to create HPO-ready DL model code using Ray Tune and MLflow and ran our first HPO experiment with Ray Tune and MLflow. Additionally, we covered how to switch to other search and scheduler algorithms once we have our HPO code framework set up, using the Optuna and HyperBand schedulers as an example. The learnings from this chapter will help you to competently carry out large-scale HPO experiments in real-life production environments, allowing you to produce high-performance DL models in a cost-effective way. We have also provided many references in the Further reading section at the end of this chapter to encourage you to study further.

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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

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Table of Contents (17) Chapters

Summary

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