Practical Machine Learning with AWS

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

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.

Part-I – Introduction to Amazon Web Services (100 Pages)

Chapter 1: AWS Concepts and Technologies

Introduction to services like S3, EC2, Identity Access Management, Roles, Load Balancer, Cloud Formation, etc.

Chapter 2: AWS Billing and PricingUnderstanding AWS pricing, billing, group and tagging, etc.

Chapter 3: AWS Cloud Security

Description about AWS compliance and artifacts, AWS Shield, Cloudwatch, Cloud Trail, etc.

Part-II – Machine Learning in AWS (300 Pages)

Chapter 4: Data Collection and Preparation

Concepts include AWS data stores, migration and helper tools. It also includes pre-processing concepts like encoding, feature engineering, missing values removal, etc.

Chapter 5: Data Modelling and Algorithms

In this section, we will talk about all the algorithms that AWS supports, including regression, clustering, classification, image, and text analytics, etc. We will then look at Sagemaker service and how to make models using it.

Chapter 6: Data Analysis and Visualization

This chapter talks about the relationship between variables, data distributions, the composition of data, etc.

Chapter 7: Model Evaluation and Optimization

This chapter talks about the monitoring of training jobs, evaluating the model accuracy, and fine-tuning models.

Chapter 8: Implementation and Operation

In this chapter, we’ll look at the deployment of models, security, and monitoring.

Chapter 9: Building a Machine Learning Workflow

In this chapter, we’ll look at the machine learning workflow in AWS .

Part-IV – Projects (100 Pages)

Chapter 10: Project – Building skills with Alexa

Chapter 11: Project - Time series forecasting using Amazon forecast

Chapter 12: Project – Modelling and deployment using XGBoost in Sagemaker

Chapter 13: Text classification using Amazon comprehend and textract

Chapter 14: Building a complete project pipeline