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Home > Data > Machine Learning > Mastering Machine Learning on AWS
Mastering Machine Learning on AWS
Mastering Machine Learning on AWS

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

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Mastering Machine Learning on AWS

Getting Started with Machine Learning for AWS

In this book, we focus on all three aspects of data science by explaining machine learning (ML) algorithms in business applications, demonstrating how they can be implemented in a scalable environment, and examining how to evaluate models and present evaluation metrics as business key performance indicators (KPIs). This book shows how Amazon Web Services (AWS) ML tools can be effectively used on large datasets. We present various scenarios where mastering ML algorithms in AWS helps data scientists to perform their jobs more effectively.

Let's take a look at the topics we will cover in this chapter:

  • How AWS empowers data scientists
  • Identifying candidate problems that can be solved using ML
  • The ML project life cycle
  • Deploying models

How AWS empowers data scientists

The number of digital data records that are stored on the internet has grown a lot in the last decade. Due to the drop in storage costs, and new sources of digital data, it is predicted that the amount of digital data stored in 2025 will be 163 zettabytes (1,630,000,000,000 terabytes). Moreover, the amount of data that is generated every day is increasing at an alarming pace, with almost 90% of current data only having been generated during the last two years. With more than 3.5 billion people with access to the internet, this data is not only generated by professionals and large companies, but also by each of the 3.5 billion internet users.

Moreover, since companies understand the importance of data, they store all of their transactional data in the hope of analyzing it and uncovering interesting trends that could help their business make important...

Identifying candidate problems that can be solved using ML

It is also important for data scientists to be able to understand the scale of data that they are working with. There might be tasks related to medical research that span thousands of patients, with hundreds of features that can be processed on a single node device. However, tasks such as advertising, where companies collect several petabytes of data on customers based on every online advertisement that is served to the user, may require several thousand machines to compute and train ML algorithms. Deep learning algorithms are GPU-intensive and require a different type of machine than other ML algorithms. In this book, for each algorithm, we supply a description of how it is implemented simply using Python libraries, and then, how it can be scaled on large AWS clusters using technologies such as Spark and AWS SageMaker...

The ML project life cycle

A typical ML project life cycle starts by understanding the problem at hand. Typically, someone in the organization (possibly a data scientist or business stakeholder) feels that some part of their business can be improved by the use of ML. For example, a music streaming company could conjecture that providing recommendations of songs similar to those played by a user would improve user engagement with the platform. Once we understand the business context and possible business actions to take, the data science team will need to consider several aspects during the project life cycle.

The following diagram describes various steps in the ML project life cycle:

Data gathering

We need to obtain data and...

Deploying models

Once we generate a model that abides by our initial KPI requirements, we need to deploy it in the production environment. This could be something as simple as creating a list of neighborhoods and political issues to address in each neighborhood, or something as complex as shipping the model to thousands of machines to make real-time decisions about which advertisements to buy for a particular marketing campaign. Once deployed to production, it is important to keep on monitoring those KPIs to make sure we're still solving the problem we aimed at initially. Sometimes, the model could have negative effects due to a change in trends, and another model needs to be trained. For instance, listeners over time may lose interest in continually hearing the same music style and the process must start all over again.

Summary

In this chapter, we first learned how AWS empowers ML practitioners and data scientists. We then looked at the various AWS tools that are available for ML, after which we learned about the ML life cycle. And finally, we touched on model deployment.

In the next chapter, we will discuss various popular ML algorithms and see how to implement them at scale on AWS. Before continuing to the next chapter, we advise readers who are new to AWS to go through the Appendix, getting started with AWS, which covers the process of creating a new AWS account.

Exercises

You can use the following exercises to learn more about ML:

  1. Define three applications you can identify on your mobile phone that implement ML. For each of the applications, define what the project life cycle was based on the steps presented in this chapter.
  2. Search for three data scientist job positions and carefully review the job requirements. For each of the requirements, classify whether the skill falls under communication, ML, or computer programming.
  3. As a data scientist, it is important to be aware of the applications around you that are generating data that can be used for ML. Based on the electronic devices you use, make a list of data that you generate every day. Define three ML applications that can use the data that you generate.
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Key benefits

  • Build machine learning apps on 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

Description

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.

Who is this book 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.

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
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Length: 306 pages
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Category :
Data
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Concepts :
Machine Learning
Tools :
Apache Spark

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Table of Contents

22 Chapters
Section 1: Machine Learning on AWS Chevron down icon Chevron up icon
Section 1: Machine Learning on AWS
Getting Started with Machine Learning for AWS Chevron down icon Chevron up icon
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
Section 2: Implementing Machine Learning Algorithms at Scale on AWS Chevron down icon Chevron up icon
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
Classifying Twitter Feeds with Naive Bayes Chevron down icon Chevron up icon
Classifying Twitter Feeds with Naive Bayes
Classification algorithms
Naive Bayes classifier
Classifying text with language models
Naive Bayes – pros and cons
Summary
Exercises
Predicting House Value with Regression Algorithms Chevron down icon Chevron up icon
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
Predicting User Behavior with Tree-Based Methods Chevron down icon Chevron up icon
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
Customer Segmentation Using Clustering Algorithms Chevron down icon Chevron up icon
Customer Segmentation Using Clustering Algorithms
Understanding how clustering algorithms work
Clustering with Apache Spark on EMR
Summary
Exercises
Analyzing Visitor Patterns to Make Recommendations Chevron down icon Chevron up icon
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
Section 3: Deep Learning Chevron down icon Chevron up icon
Section 3: Deep Learning
Implementing Deep Learning Algorithms Chevron down icon Chevron up icon
Implementing Deep Learning Algorithms
Understanding deep learning
Applications of deep learning
Understanding deep learning algorithms
Understanding convolutional neural networks
Summary
Exercises
Implementing Deep Learning with TensorFlow on AWS Chevron down icon Chevron up icon
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
Image Classification and Detection with SageMaker Chevron down icon Chevron up icon
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
Section 4: Integrating Ready-Made AWS Machine Learning Services Chevron down icon Chevron up icon
Section 4: Integrating Ready-Made AWS Machine Learning Services
Working with AWS Comprehend Chevron down icon Chevron up icon
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
Using AWS Rekognition Chevron down icon Chevron up icon
Using AWS Rekognition
Introducing Amazon Rekognition
Implementing object and scene detection
Implementing facial analysis
Summary
Exercises
Building Conversational Interfaces Using AWS Lex Chevron down icon Chevron up icon
Building Conversational Interfaces Using AWS Lex
Introducing Amazon Lex
Building a custom chatbot using Amazon Lex
Summary
Exercises
Section 5: Optimizing and Deploying Models through AWS Chevron down icon Chevron up icon
Section 5: Optimizing and Deploying Models through AWS
Creating Clusters on AWS Chevron down icon Chevron up icon
Creating Clusters on AWS
Choosing your instance types
Distributed deep learning
Summary
Optimizing Models in Spark and SageMaker Chevron down icon Chevron up icon
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
Tuning Clusters for Machine Learning Chevron down icon Chevron up icon
Tuning Clusters for Machine Learning
Introduction to the EMR architecture
Tuning EMR for different applications
Managing data pipelines with Glue
Summary
Deploying Models Built in AWS Chevron down icon Chevron up icon
Deploying Models Built in AWS
SageMaker model deployment
Apache Spark model deployment
Summary
Exercises
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Eduk79 Aug 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Awesome book! If you want to unleash the power of the AWS platform and use it for building and running ML models, then this book is definitely a must have. The authors give solid explanations and provide clear examples along the book, aiding the reader with downloadable Python notebooks that contribute to a better understanding of every topic. Mostly recommended for anyone trying to grasp the ML concepts and learn how to apply them to build real stuff.
Amazon Verified review Amazon
W. Simmons Jun 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an excellent overview of all major machine learning techniques in AWS with practical examples. It contains a background on the math behind the machine learning as well as step-by-step guidance using helpful real-world examples. The examples are wide-ranging; They include text classification, customer segmentation, image recognition, recommendations, and natural language processing of text. I found examples to be thorough and helpful. The mathematical techniques covered range from Naive Bayes classification to deep learning.For data engineers, there is quite a bit of content on automation and tuning of the machine learning process, data pipelines, and cluster configurations. While it takes many years to become a master of practical applied machine learning on cloud, this book is a great way to get started. Highly recommended for current practitioners who want to learn more or beginners who are taking the dive into machine learning and AI on AWS!
Amazon Verified review Amazon
matteo Jun 09, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Ottimo libro scritto in modo molto comprensibile
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mr_anderson Jan 24, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for learning practical ML implementation on AWS.
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Snejana Sep 27, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a Data Science mentor, I always look for the best resource that can get aspiring data scientists ready to join the industry ranks. This book is excellent as it covers the content that is frequently pushed out of scope in the standard data science curriculum. Working with AWS services tends to be omitted and it leaves a gap in students preparedness. This addresses this gap and more! It also does not shy with Big Data by providing practical advice with distributed systems such as Spark, Presto, Hive and others. The content of the book is well thought and it will serve as a good reference to not only incoming data scientists and engineers, but also current professionals who would like to extend their knowledge.
Amazon Verified review Amazon

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About the authors

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Profile icon Dr. Saket S.R. Mengle
Dr. Saket S.R. Mengle
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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.
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Profile icon Maximo Gurmendez
Maximo Gurmendez
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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.
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  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela