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Full Description
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
Contents
Part I: Introduction to Amazon Web Services.- Chapter 1: Cloud Computing and AWS.- Chapter 2: AWS Pricing and Cost Management.- Chapter 3: Security in Amazon Web Services.- Part II: Machine Learning in AWS.- Chapter 4: Introduction to Machine Learning.- Chapter 5: Data Processing in AWS.- Chapter 6: Building and Deploying Models in SageMaker.- Chapter 7: Using CloudWatch in SageMaker.- Chapter 8: Running a Custom Algorithm in SageMaker.- Chapter 9: Making an End-to-End Pipeline in SageMaker.- Part III: Other AWS Services.- Chapter 10: Machine Learning Use Cases in AWS.- Appendix A: Creating a Root User Account to Access Amazon Management Console.- Appendix B: Creating an IAM Role.- Appendix C: .Creating an IAM User- Appendix D: Creating an S3 Bucket.- Appendix E: Creating a SageMaker Notebook Instance.-
