Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
Guanhua Wang
Reducing time costs 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.
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.
Thể loại:
Năm:
2022
Nhà xuát bản:
Packt Publishing
Ngôn ngữ:
english
Trang:
284
ISBN 10:
1801815690
ISBN 13:
9781801815697
File:
PDF, 4.39 MB
IPFS:
,
english, 2022
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