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    Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autor:
    Guanhua Wang
    Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems Guanhua Wang - okladka książki

    Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems Guanhua Wang - okladka książki

    Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems Guanhua Wang - audiobook MP3

    Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems Guanhua Wang - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    284
    Dostępne formaty:
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    Ebook (125,10 zł najniższa cena z 30 dni)

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    Do przechowalni

    Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.

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    O autorze książki

    Guanhua Wang is a final-year Computer Science PhD student in the RISELab at UC Berkeley, advised by Professor Ion Stoica. His research lies primarily in the Machine Learning Systems area including fast collective communication, efficient in-parallel model training and real-time model serving. His research gained lots of attention from both academia and industry. He was invited to give talks to top-tier universities (MIT, Stanford, CMU, Princeton) and big tech companies (Facebook/Meta, Microsoft). He received his master’s degree from HKUST and bachelor’s degree from Southeast University in China. He also did some cool research on wireless networks. He likes playing soccer and runs half-marathon multiple times in the Bay Area of California.

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