English | 2019 | ISBN: 1789349796 | 306 pages | True Retail (EPUB) | 20.4 MB

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.

Key Features
Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlow
Learn model optimization, and understand how to scale your models using simple and secure APIs
Develop, train, tune and deploy neural network models to accelerate model performance in the cloud

Book Description
AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.
As you go through the chapters, you'll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis.
By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
What you will learn
Manage AI workflows by using AWS cloud to deploy services that feed smart data products
Use SageMaker services to create recommendation models
Scale model training and deployment using Apache Spark on EMR
Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker
Build deep learning models on AWS using TensorFlow and deploy them as services
Enhance your apps by combining Apache Spark and Amazon SageMaker

Who this book is for
This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Table of Contents
Getting started with Machine learning for AWS
Classifying Twitter Feeds with Naive Bayes
Predicting House Value with Regression Algorithms
Predicting User Behavior with Tree-based Methods
Customer Segmentation Using Clustering Algorithms
Analyzing Visitor Patterns to Make Recommendations
Implementing Deep Learning Algorithms
Implementing Deep Learning with TensorFlow on AWS
Image Classification and Detection with Sagemaker
Working with AWS Comprehend
Using AWS Rekognition
Building Conversational Interfaces Using AWS Lex
Creating Clusters on AWS
Optimizing Models in Spark and Sagemaker
Tuning clusters for Machine Learning
Deploying models built on AWS

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