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
Tabla de Contenidos:
  • Table of Contents Deep Learning Life Cycle and MLOps Challenges Getting Started with MLflow for Deep Learning Tracking Models, Parameters, and Metrics Tracking Code and Data Versioning Running DL Pipelines in Different Environments Running Hyperparameter Tuning at Scale Multi-Step Deep Learning Inference Pipeline Deploying a DL Inference Pipeline at Scale Fundamentals of Deep Learning Explainability Implementing DL Explainability with MLflow.

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