Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
Accelerate model training and inference with order-of-magnitude time reduction.
Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
товар №: 89211441

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

79% of respondents would recommend this to a friend

товар №: 89211441

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€ 54

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What Stands Out

Distributed Training
Enhances model training efficiency by leveraging multiple nodes, significantly reducing time and resources required to achieve optimal performance.
Scalable Serving
Facilitates seamless deployment of models across distributed systems, ensuring high availability and reliability, ideal for handling large-scale user demands.
Python Integration
Utilizes familiar Python libraries, making it accessible for data scientists and developers, promoting rapid development and ease of use in machine learning projects.

Информация о продукте

Shop Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems online at a best price in Estonia. 1801815690
  • Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook DescriptionReducing 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.What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is forThis book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.Table of ContentsSplitting Input Data Parameter Server and All-ReduceBuilding a Data Parallel Training and Serving PipelineBottlenecks and SolutionsSplitting the ModelPipeline Input and Layer SplitImplementing Model Parallel Training and Serving WorkflowsAchieving Higher Throughput and Lower LatencyA Hybrid of Data and Model ParallelismFederated Learning and Edge DevicesElastic Model Training and ServingAdvanced Techniques for Further Speed-Ups
Publisher Packt Publishing
Publication date April 29, 2022
Language English
Print length 284 pages
ISBN-10 1801815690
ISBN-13 978-1801815697
Item Weight 1.08 pounds (490 grams)
Dimensions 7.5 x 0.64 x 9.25 inches (19.1 x 1.6 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance their model training speed and efficiency using distributed systems.

  • Machine Learning Engineers

    Great for engineers wanting to implement large-scale distributed machine learning solutions in production environments.

  • Research Professionals

    Useful for researchers needing to quickly prototype and test distributed ML algorithms on large datasets.

Not Suitable For
  • Beginners

    Not suitable for beginners in machine learning who may struggle with complex distributed system concepts.

ОПИСАНИЕ ТОВАРА

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AI & Machine Learning Editorial Review

Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems is a valuable resource for those keen on delving into Distributed Training. Although it provides comprehensive coverage of data parallelism, model synchronization, and bottlenecks, some readers found the writing quality lacking due to grammatical issues and incomplete code examples, particularly in TensorFlow. The book effectively discusses the necessity of distributed computing in modern ML but may lack in-depth discussions. Nonetheless, it offers a strong technical foundation for understanding the principles and techniques essential in leveraging distributed systems for efficient model training.

Customer Reviews & Ratings

3.8
19 оценки клиентов
  • 5 звезда
    55%
  • 4 звезда
    12%
  • 3 звезда
    12%
  • 2 звезда
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  • 1 звезда
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Плюсы

  • Covers critical concepts of distributed machine learning
  • Excellent overview of data processing requirements
  • Provides solid technical foundation for understanding
  • Discusses pros and cons of distributed approaches
  • Introduces strategies for parallel training and serving

Минусы

  • May feel overwhelming for beginners in ML or software engineering

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Особенности и преимущества

  • Learn to build efficient data processing pipelines for machine learning.
  • Reduce model training and inference time significantly.
  • Master advanced parallel schemes in TensorFlow and PyTorch.
  • Identify and mitigate bottlenecks in distributed training.
  • Utilize state-of-the-art hardware for optimized performance.
  • Gain practical skills for implementing distributed machine learning systems.

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