Building Scalable deep learning pipelines on AWS: develop, train, and deploy deep learning models
Testas, Abdelaziz
Building Scalable deep learning pipelines on AWS: develop, train, and deploy deep learning models - New York Apress 2024 - xx, 760 p.
Table of contents:
Front Matter
Pages i-xx
Download chapter PDF
Overview of Scalable Deep Learning Pipelines on AWS
Abdelaziz Testas
Pages 1-56
Setting Up a Deep Learning Environment on AWS
Abdelaziz Testas
Pages 57-113
Data Preparation with PySpark for Deep Learning
Abdelaziz Testas
Pages 115-211
Deep Learning with PyTorch for Regression
Abdelaziz Testas
Pages 213-273
Deep Learning with TensorFlow for Regression
Abdelaziz Testas
Pages 275-319
Deep Learning with PyTorch for Classification
Abdelaziz Testas
Pages 321-429
Deep Learning with TensorFlow for Classification
Abdelaziz Testas
Pages 431-488
Scalable Deep Learning Pipelines with Apache Airflow
Abdelaziz Testas
Pages 489-584
Techniques for Improving Model Performance
Abdelaziz Testas
Pages 585-701
Deploying and Monitoring Deep Learning Models
Abdelaziz Testas
Pages 703-738
Back Matter
Pages 739-760
[https://link.springer.com/book/10.1007/979-8-8688-1017-6]
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.
(https://link.springer.com/book/10.1007/979-8-8688-1017-6)
9798868810169
Deep learning models--AWS
006.31 / TES
Building Scalable deep learning pipelines on AWS: develop, train, and deploy deep learning models - New York Apress 2024 - xx, 760 p.
Table of contents:
Front Matter
Pages i-xx
Download chapter PDF
Overview of Scalable Deep Learning Pipelines on AWS
Abdelaziz Testas
Pages 1-56
Setting Up a Deep Learning Environment on AWS
Abdelaziz Testas
Pages 57-113
Data Preparation with PySpark for Deep Learning
Abdelaziz Testas
Pages 115-211
Deep Learning with PyTorch for Regression
Abdelaziz Testas
Pages 213-273
Deep Learning with TensorFlow for Regression
Abdelaziz Testas
Pages 275-319
Deep Learning with PyTorch for Classification
Abdelaziz Testas
Pages 321-429
Deep Learning with TensorFlow for Classification
Abdelaziz Testas
Pages 431-488
Scalable Deep Learning Pipelines with Apache Airflow
Abdelaziz Testas
Pages 489-584
Techniques for Improving Model Performance
Abdelaziz Testas
Pages 585-701
Deploying and Monitoring Deep Learning Models
Abdelaziz Testas
Pages 703-738
Back Matter
Pages 739-760
[https://link.springer.com/book/10.1007/979-8-8688-1017-6]
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.
(https://link.springer.com/book/10.1007/979-8-8688-1017-6)
9798868810169
Deep learning models--AWS
006.31 / TES