Practical deep learning at scale with MLflow : bridge the gap between offline experimentation and online production

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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Detalles Bibliográficos
Otros Autores: Liu, Yong, author (author), Zaharia, Matei, writer of foreword (writer of foreword)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai : Packt [2022]
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009670622206719

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