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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 Yong Liu
Profile icon Yong Liu
LinkedIn
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.
See other products by 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

Chapter 4: Tracking Code and Data Versioning

DL models are not just models – they are intimately tied to the code that trains and tests the model and the data that's used for training and testing. If we don't track the code and data that's used for the model, it is impossible to reproduce the model or improve it. Furthermore, there have been recent industry-wide awakenings and paradigm shifts toward a data-centric AI (https://www.forbes.com/sites/gilpress/2021/06/16/andrew-ng-launches-a-campaign-for-data-centric-ai/?sh=5cbacdc574f5), where the importance of data is being lifted to a first-class artifact in building ML and, especially, DL models. Due to this, in this chapter, we will learn how to track code and data versioning using MLflow. We will learn about the different ways we can track code and pipeline versioning and how to use Delta Lake for data versioning. By the end of this chapter, you will be able to understand and implement tracking techniques for...

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Authors (1)

Profile icon Yong Liu
LinkedIn
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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See other products by Yong Liu

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