Practical Machine Learning with AWS

Successfully build, tune, and deploy any machine learning model to production, and automate the process from data processing to deployment. 
This book is divided into three parts. Part 1 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 2 covers machine learning in AWS in detail and discusses services such as Sagemaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models (including Amazon Comprehend, which is a natural language processing service that uses machine learning to find insights and relationships in text, and Amazon Forecast, which helps you deliver highly accurate forecasts). It also covers fine-tuning and deploying models. In Part 3 you will learn how to implement five machine learning projects such as Building Skills with Alexa, Time Series Forecasting using Amazon Forecast, and modeling and deployment using XGBoost in Sagemaker from scratch using the services provided by AWS.
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 also will help you prepare for machine learning specialty AWS certification.

What You Will Learn

• Be familiar with the different machine learning services offered by AWS 
• Know how to use AWS S3, EC2, Identity Access Management, and Cloud Formation
• Know how to use AWS Sagemaker, Amazon Comprehend, and Amazon Forecast
• Build skills with Alexa
• 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