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Building scalable deep learning pipe...

Amazon Web Services (Firm)

Building scalable deep learning pipelines on AWSdevelop, train, and deploy deep learning models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building scalable deep learning pipelines on AWSby Abdelaziz Testas.
Reminder of title:	develop, train, and deploy deep learning models /
Author:	Testas, Abdelaziz.
Published:	Berkeley, CA :Apress :2024.
Description:	xx, 760 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/979-8-8688-1017-6
ISBN:	9798868810176$q(electronic bk.)

Building scalable deep learning pipelines on AWSdevelop, train, and deploy deep learning models /
Testas, Abdelaziz.

Building scalable deep learning pipelines on AWSdevelop, train, and deploy deep learning models /[electronic resource] :by Abdelaziz Testas. - Berkeley, CA :Apress :2024. - xx, 760 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Overview of Scalable Deep Learning Pipelines on AWS -- Chapter 2: Setting Up a Deep Learning Environment on AWS -- Chapter 3: Data Preparation with PySpark for Deep Learning -- Chapter 4: Deep Learning with PyTorch for Regression -- Chapter 5: Deep Learning with TensorFlow for Regression -- Chapter 6: Deep Learning with PyTorch for Classification -- Chapter 7: Deep Learning with TensorFlow for Classification -- Chapter 8: Scalable Deep Learning Pipelines with Apache Airflow -- Chapter 9: Techniques for Improving Model Performance -- Chapter 10: Deploying and Monitoring Deep Learning Models.

This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies-such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3-to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will Learn Maximize AWS services for scalable and high-performance deep learning architectures Harness the capacity of PyTorch and TensorFlow for advanced neural network development Utilize PySpark for efficient distributed data processing on AWS Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deployment.

ISBN: 9798868810176$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1017-6doiSubjects--Corporate Names:

785206
Amazon Web Services (Firm)
Subjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .T47 2024

Dewey Class. No.: 006.31

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505 0 $a Chapter 1: Overview of Scalable Deep Learning Pipelines on AWS -- Chapter 2: Setting Up a Deep Learning Environment on AWS -- Chapter 3: Data Preparation with PySpark for Deep Learning -- Chapter 4: Deep Learning with PyTorch for Regression -- Chapter 5: Deep Learning with TensorFlow for Regression -- Chapter 6: Deep Learning with PyTorch for Classification -- Chapter 7: Deep Learning with TensorFlow for Classification -- Chapter 8: Scalable Deep Learning Pipelines with Apache Airflow -- Chapter 9: Techniques for Improving Model Performance -- Chapter 10: Deploying and Monitoring Deep Learning Models.
520 $a This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies-such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3-to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will Learn Maximize AWS services for scalable and high-performance deep learning architectures Harness the capacity of PyTorch and TensorFlow for advanced neural network development Utilize PySpark for efficient distributed data processing on AWS Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deployment.
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