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Practical Machine Learning with AWS - Process, Build, Deploy, and Productionize Your Models Using AWS (Paperback, 1st ed.)
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Practical Machine Learning with AWS - Process, Build, Deploy, and Productionize Your Models Using AWS (Paperback, 1st ed.)
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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. 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
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