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

Tracking model provenance

Provenance tracking for digital artifacts has been long studied in the literature. For example, when you're using a piece of patient diagnosis data in the biomedical industry, people usually want to know where it comes from, what kind of processing and cleaning has been done to the data, who owns the data, and other history and lineage information about the data. The rise of ML/DL models for industrial and business scenarios in production makes provenance tracking a required functionality. The different granularities of provenance tracking are critical for operationalizing and managing not just the data science offline experimentation, but also before/during/after the model is deployed in production. So, what needs to be tracked for provenance?

Understanding the open provenance tracking framework

Let's look at a general provenance tracking framework to understand the big picture of why provenance tracking is a major effort. The following diagram...

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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

Tracking model provenance

Understanding the open provenance tracking framework

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 model provenance

Understanding the open provenance tracking framework

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Authors (1)

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