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 MLflow Key Features Focus on deep learning models and MLflow to develop practical business AI solutions at scale Ship deep learning pipelines from experim...

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Bibliographische Detailangaben

1. Verfasser:	Liu, Yong (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Birmingham ; Mumbai Packt Publishing, Limited 2022
Ausgabe:	First published
Schlagworte:	Machine learning Electronic books COMPUTERS / Computer Engineering Deep learning (Machine learning)
Online-Zugang:	FHA01 FHR01 FLA01 UBY01
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