Mastering Machine Learning on AWS

Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

Dr. Saket S.R. Mengle , Maximo Gurmendez 지음

Packt(GCO Science) 2019년 05월 20일 출간

작품소개

책소개

▶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

▶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

▶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.

목차

▶TABLE of CONTENTS
1. Getting Started with Machine Learning for AWS
2. Classifying Twitter Feeds with Naive Bayes
3. Predicting House Value with Regression Algorithms
4. Predicting User Behavior with Tree-Based Methods
5. Customer Segmentation Using Clustering Algorithms
6. Analyzing Visitor Patterns to Make Recommendations
7. Implementing Deep Learning Algorithms
8. Implementing Deep Learning with TensorFlow on AWS
9. Image Classification and Detection with SageMaker
10. Working with AWS Comprehend
11. Using AWS Rekognition
12. Building Conversational Interfaces Using AWS Lex
13. Creating Clusters on AWS
14. Optimizing Models in Spark and SageMaker
15. Tuning Clusters for Machine Learning
16. Deploying Models Built in AWS

책 속으로

▶What this book covers
- Chapter 1, Getting Started with Machine Learning for AWS, introduces machine learning to the readers. It explains why it is necessary for data scientists to learn about machine learning and how AWS can help them to solve various real-world problems. We also discuss the AWS services and tools that we will be covered in the book.

- Chapter 2, Classifying Twitter Feeds with Naive Bayes, introduces the basics of the Naive Bayes algorithm and presents a text classification problem that will be addressed by the use of this algorithm and language models. We'll provide examples explaining how to apply Naive Bayes using scikit-learn and Apache Spark on SageMaker's BlazingText. Additionally, we'll explore how to use the ideas behind Bayesian reasoning in more complex scenarios. We will use the Twitter API to stream tweets from two different political candidates and predict who wrote them. We will use scikit-learn, Apache Spark, SageMaker, and BlazingText.

- Chapter 3, Predicting House Value with Regression Algorithms, introduces the basics of regression algorithms and applies them to predict the price of houses given a number of features. We'll also introduce how to use logistic regression for classification problems. Examples in SageMaker for scikit-learn and Apache Spark will be provided. We'll be using the Boston Housing Price dataset https://www.kaggle.com/c/boston-housing/ , along with scikit-learn, Apache Spark, and SageMaker.

- Chapter 4, Predicting User Behavior with Tree-Based Methods, introduces decision trees, random forests, and gradient boosted trees. We will explore how to use these algorithms to predict when users will click on ads. Additionally, we will explain how to use AWS EMR and Apache Spark to engineer models at a large scale. We will use the Adform click prediction dataset (https://doi.org/10.7910/DVN/ TADBY7, Harvard Dataverse, V2). We will use the xgboost, Apache Spark, SageMaker, and EMR libraries.

- Chapter 5, Customer Segmentation Using Clustering Algorithms, introduces the main clustering algorithms by exploring how to apply them for customer segmentation based on consumer patterns. Through AWS SageMaker, we will show how to run these algorithms in skicit-learn and Apache Spark. We will use the e-commerce data from Fabien Daniel (https://www.kaggle.com/fabiendaniel/customer-segmentation/data) and scikit-learn, Apache Spark, and SageMaker.

- Chapter 6, Analyzing Visitor Patterns to Make Recommendations, presents the problem of finding similar users based on their navigation patterns in order to recommend custom marketing strategies. Collaborative filtering and distance-based methods will be introduced with examples in scikit-learn and Apache Spark on AWS SageMaker. We will use Kwan Hui Lim's Theme Park Attraction Visits Dataset (https://sites.google.com/site/limkwanhui/datacode), Apache Spark, and SageMaker.

- Chapter 7, Implementing Deep Learning Algorithms, introduces the reader to the main concepts behind deep learning and explains why it has become so relevant in today's AIpowered products. The aim of this chapter is to not discuss the theoretical details of deep learning, but to explain the algorithms with examples and provide a high-level conceptual understanding of deep learning algorithms. This will give the readers a platform to understand what they are implementing in the next chapters.

- Chapter 8, Implementing Deep Learning with TensorFlow on AWS, goes through a series of practical image-recognition problems and explains how to address them with TensorFlow on AWS. TensorFlow is a very popular deep learning framework that can be used to train deep neural networks. This chapter will explain how TensorFlow can be installed by readers and used to train deep learning models using toy datasets. In this chapter, we'll use the MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/), along with TensorFlow and SageMaker.

...

출판사 서평

▶ Preface
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.

작가정보

저자(글) Dr. Saket S.R. Mengle

Dr. Saket S.R. Mengle holds a PhD in text mining from Illinois Institute of Technology, Chicago. He has worked in a variety of fields, including text classification, information retrieval, large-scale machine learning, and linear optimization. He currently works as senior principal data scientist at dataxu, where he is responsible for developing and maintaining the algorithms that drive dataxu's real-time advertising platform.

저자(글) Maximo Gurmendez

Maximo Gurmendez holds a master's degree in computer science/AI from Northeastern University, where he attended as a Fulbright Scholar. Since 2009, he has been working with dataxu as data science engineering lead. He's also the founder of Montevideo Labs (a data science and engineering consultancy). Additionally, Maximo is a computer science professor at the University of Montevideo and is director of its data science for business program.