Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow (English Edition)

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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