Practical Deep Learning at Scale with MLflow
Bridge the gap between offline experimentation and online production
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey Features:Focus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook Description:The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: d…
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Produktdetails
- ISBN: 978-1-80324-133-3
- EAN: 9781803241333
- Produktnummer: 39623107
- Verlag: Packt Publishing
- Sprache: Englisch
- Erscheinungsjahr: 2022
- Seitenangabe: 288 S.
- Masse: H23.5 cm x B19.1 cm x D1.5 cm 543 g
- Abbildungen: Paperback
- Gewicht: 543
Über den Autor
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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