Book Image

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle, Maximo Gurmendez
Book Image

Mastering Machine Learning on AWS

By: Dr. Saket S.R. Mengle, Maximo Gurmendez

Overview of this book

Amazon Web Services (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 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.

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Table of Contents (24 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Machine Learning on AWS
Section 1: Machine Learning on AWS
2
Getting Started with Machine Learning for AWS
Getting Started with Machine Learning for AWS
How AWS empowers data scientists
Identifying candidate problems that can be solved using ML
The ML project life cycle
Deploying models
Summary
Exercises
3
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
4
Classifying Twitter Feeds with Naive Bayes
Classifying Twitter Feeds with Naive Bayes
Classification algorithms
Naive Bayes classifier
Classifying text with language models
Naive Bayes – pros and cons
Summary
Exercises
5
Predicting House Value with Regression Algorithms
Predicting House Value with Regression Algorithms
Predicting the price of houses
Understanding linear regression
Evaluating regression models
Implementing linear regression through scikit-learn
Implementing linear regression through Apache Spark
Implementing linear regression through SageMaker's Linear Learner
Understanding logistic regression
Pros and cons of linear models
Summary
6
Predicting User Behavior with Tree-Based Methods
Predicting User Behavior with Tree-Based Methods
Understanding decision trees
Understanding random forest algorithms
Understanding gradient-boosting algorithms
Predicting clicks on log streams
Summary
Exercises
7
Customer Segmentation Using Clustering Algorithms
Customer Segmentation Using Clustering Algorithms
Understanding how clustering algorithms work
Clustering with Apache Spark on EMR
Summary
Exercises
8
Analyzing Visitor Patterns to Make Recommendations
Analyzing Visitor Patterns to Make Recommendations
Making theme park attraction recommendations through Flickr data
Collaborative filtering
Finding recommendations through Apache Spark's ALS
Recommending attractions through SageMaker FMs
Summary
Exercises
9
Section 3: Deep Learning
Section 3: Deep Learning
10
Implementing Deep Learning Algorithms
Implementing Deep Learning Algorithms
Understanding deep learning
Applications of deep learning
Understanding deep learning algorithms
Understanding convolutional neural networks
Summary
Exercises
11
Implementing Deep Learning with TensorFlow on AWS
Implementing Deep Learning with TensorFlow on AWS
Introducing TensorFlow
TensorFlow as a general machine learning library
Training and serving the TensorFlow model through SageMaker
Creating a custom neural net with TensorFlow
Summary
Exercises
12
Image Classification and Detection with SageMaker
Image Classification and Detection with SageMaker
Introducing Amazon SageMaker for image classification
Training a deep learning model using Amazon SageMaker
Classifying images using Amazon SageMaker
Summary
Exercises
13
Section 4: Integrating Ready-Made AWS Machine Learning Services
Section 4: Integrating Ready-Made AWS Machine Learning Services
14
Working with AWS Comprehend
Working with AWS Comprehend
Introducing Amazon Comprehend
Accessing Amazon Comprehend
Named-entity recognition using Comprehend
Sentiment analysis using Comprehend
Text classification using Comprehend
Summary
Exercises
15
Using AWS Rekognition
Using AWS Rekognition
Introducing Amazon Rekognition
Implementing object and scene detection
Implementing facial analysis
Summary
Exercises
16
Building Conversational Interfaces Using AWS Lex
Building Conversational Interfaces Using AWS Lex
Introducing Amazon Lex
Building a custom chatbot using Amazon Lex
Summary
Exercises
17
Section 5: Optimizing and Deploying Models through AWS
Section 5: Optimizing and Deploying Models through AWS
18
Creating Clusters on AWS
Creating Clusters on AWS
Choosing your instance types
Distributed deep learning
Summary
19
Optimizing Models in Spark and SageMaker
Optimizing Models in Spark and SageMaker
The importance of model optimization
Automatic hyperparameter tuning
Hyperparameter tuning in Apache Spark
Hyperparameter tuning in SageMaker
Summary
Exercises
20
Tuning Clusters for Machine Learning
Tuning Clusters for Machine Learning
Introduction to the EMR architecture
Tuning EMR for different applications
Managing data pipelines with Glue
Summary
21
Deploying Models Built in AWS
Deploying Models Built in AWS
SageMaker model deployment
Apache Spark model deployment
Summary
Exercises
22
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Appendix: Getting Started with AWS
Appendix: Getting Started with AWS

Automatic hyperparameter tuning

The simplest way to perform hyperparameter tuning is called grid search. We define different values that we would like to try for each hyperparameter; for example, if we are training trees, we may want to try depths of 5, 10, and 15. At the same time, we'd like to see whether the best impurity measure is information gain or gini. This creates a total of six combinations that have to be tested for accuracy. As you might be anticipating, the number of combinations will grow exponentially with the number of hyperparameters to consider. For this reason, other techniques are used to avoid testing all possible combinations. A simple approach is to randomize the combinations being tried. Some combinations will be missed, but some variations will be tested without an inductive bias.

AWS SageMaker provides a service for hyperparameter tuning that is...

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