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You're reading from  Practical Deep Learning at Scale with MLflow

Product typeBook
Published inJul 2022
PublisherPackt
ISBN-139781803241333
Edition1st Edition
Concepts
Deep Learning
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Author (1)
Yong Liu
Yong Liu
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Yong Liu

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.
Read more about Yong Liu

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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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Practical Deep Learning at Scale with MLflow
Published in: Jul 2022Publisher: PacktISBN-13: 9781803241333
© 2022 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Yong Liu

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.
Read more about Yong Liu

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