Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWS - Softcover

Singh, Himanshu

Softcover

ISBN 10: 1484262212 ISBN 13: 9781484262214

Verlag: Apress, 2020

Inhaltsangabe

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. 

This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract.

By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning―Specialty certification exam.

What You Will Learn

• Be familiar with the different machine learning services offered by AWS 
• Understand S3, EC2, Identity Access Management, and Cloud Formation
• Understand SageMaker, Amazon Comprehend, and Amazon Forecast
• Execute live projects: from the pre-processing phase to deployment on AWS

Who This Book Is For

Machine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification

Über diesen Titel

Über die Autorin bzw. den Autor

Himanshu Singh is Technology Lead and Senior NLP Engineer at Legato Healthcare (an Anthem Company). He has seven years of experience in the AI industry, primarily in computer vision and natural language processing. He has authored three books on machine learning. He has an MBA from Narsee Monjee Institute of Management Studies, and a postgraduate diploma in Applied Statistics.

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Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. 

This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract.

By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning―Specialty certification exam.

You will:

• Be familiar with the different machine learning services offered by AWS 
• Understand S3, EC2, Identity Access Management, and Cloud Formation
• Understand SageMaker, Amazon Comprehend, and Amazon Forecast
• Execute live projects: from the pre-processing phase to deployment on AWS

Details zur Ausgabe

• Verlag: Apress
• Erscheinungsdatum: 2020
• Sprache: Englisch
• ISBN 10: 1484262212
• ISBN 13: 9781484262214
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 258
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland