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

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Deploying using Ray Serve and MLflow deployment plugins

A more generic way to do deployment is to use a framework such as Ray Serve (https://docs.ray.io/en/latest/serve/index.html). Ray Serve has several advantages, such as DL model frameworks agnostics, native Python support, and supporting complex model composition inference patterns. Ray Serve supports all major DL frameworks and any arbitrary business logic. So, can we leverage both Ray Serve and MLflow to do model deployment and serve? The good news is that we can use the MLflow deployment plugins provided by Ray Serve to do this. Let's walk through how to use the mlflow-ray-serve plugin to do MLflow model deployment using Ray Serve (https://github.com/ray-project/mlflow-ray-serve). Before we begin, we need to install the mlflow-ray-serve package: