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Practical Deep Learning Scale by Yong Liu (12 results)
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Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production (Paperback or Softback)
Published by Packt Publishing 7/8/2022, 2022
ISBN 10: 1803241330 ISBN 13: 9781803241333
Language: English
Paperback or Softback. Condition: New. Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production 1.1. Book.
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Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production
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Add to basketpaperback. Condition: Sehr gut. 288 Seiten; 9781803241333.2 Gewicht in Gramm: 1.
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Add to basketCondition: New. Über den AutorrnrnYong 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 princ.
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Practical Deep Learning at Scale with MLflow
Published by Packt Publishing Limited, 2022
ISBN 10: 1803241330 ISBN 13: 9781803241333
Language: English
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Practical Deep Learning at Scale with MLflow
Published by Packt Publishing Limited, 2022
ISBN 10: 1803241330 ISBN 13: 9781803241333
Language: English
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production
Published by Packt Publishing Limited, 2022
ISBN 10: 1803241330 ISBN 13: 9781803241333
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.
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Practical Deep Learning at Scale with MLflow
Published by Packt Publishing, Limited, 2022
ISBN 10: 1803241330 ISBN 13: 9781803241333
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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: data, model, code, and explainability and the role of MLflow in these areas.From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What You Will Learn:Understand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is for:This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
