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

16. Other Books You May Enjoy

Tracking data versioning in Delta Lake

In this section, we'll learn how data is tracked in MLflow. Historically, data management and versioning are usually considered as being different from machine learning and data science. However, the advent of data-centric AI is playing an increasingly important role, particularly in DL. Therefore, it is critical to know what and how data is being used to improve the DL model. In the first data-centric AI competition, which was organized by Andrew Ng in the summer of 2021, the requirements to become a winner were not about changing and tuning a model, but rather improving the dataset of a fixed model (https://https-deeplearning-ai.github.io/data-centric-comp/). Here is a quote from the competition's web page:

"The Data-Centric AI Competition inverts the traditional format and asks you to improve a dataset, given a fixed model. We will provide you with a dataset to improve by applying data-centric techniques such as fixing incorrect...

The rest of the chapter is locked

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

Tracking data versioning in Delta Lake

Authors (1)

Personalised recommendations for you

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

Tracking data versioning in Delta Lake

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you