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Product typeBook
Published inJul 2022
PublisherPackt
ISBN-139781803241333
Edition1st Edition
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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.
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Understanding different deployment tools and host environments

There are different deployment tools in the MLOps technology stack that have different target use cases and host environments for deploying different model inference pipelines. In Chapter 7, Multi-Step Deep Learning Inference Pipeline, we learned the different inference scenarios and requirements and implemented a multi-step DL inference pipeline that can be deployed into a model hosting/serving environment. Now, we will learn how to deploy such a model to a few specific model hosting and serving environments. This is visualized in Figure 8.1 as follows:

Figure 8.1 – Using model deployment tools to deploy a model inference pipeline to a model hosting and serving environment

As can be seen from Figure 8.1, there can be different deployment tools for different model hosting and serving environments. Here, we list the three typical scenarios as follows:

  • Batch inference at scale: If we...
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Practical Deep Learning at Scale with MLflow
Published in: Jul 2022Publisher: PacktISBN-13: 9781803241333

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