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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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Profile Icon Dr. Saket S.R. Mengle Profile Icon Maximo Gurmendez
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (8 Ratings)
Paperback May 2019 306 pages 1st Edition
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Can$27.98 Can$39.99
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Arrow left icon
Profile Icon Dr. Saket S.R. Mengle Profile Icon Maximo Gurmendez
Arrow right icon
Free Trial
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (8 Ratings)
Paperback May 2019 306 pages 1st Edition
eBook
Can$27.98 Can$39.99
Paperback
Can$49.99
Subscription
Free Trial
eBook
Can$27.98 Can$39.99
Paperback
Can$49.99
Subscription
Free Trial

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Mastering Machine Learning on AWS

Section 1: Machine Learning on AWS

The objective of this section is to introduce readers to machine learning in the context of AWS cloud computing and services. We expect our audience to have some basic knowledge of machine learning. However, we'll describe the nature of a typically successful machine learning project, and the challenges often faced. We will provide an overview of the different AWS services, along with examples of typical machine learning pipelines and the key aspects to consider in order to create smart AI-powered products.

This section contains the following chapter:

  • Chapter 1, Getting Started with Machine Learning for AWS
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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 20, 2019
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781789349795
Vendor :
Amazon
Category :
Languages :

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Product Details

Publication date : May 20, 2019
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781789349795
Vendor :
Amazon
Category :
Languages :

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Table of Contents

22 Chapters
Section 1: Machine Learning on AWS Chevron down icon Chevron up icon
Getting Started with Machine Learning for AWS Chevron down icon Chevron up icon
Section 2: Implementing Machine Learning Algorithms at Scale on AWS Chevron down icon Chevron up icon
Classifying Twitter Feeds with Naive Bayes Chevron down icon Chevron up icon
Predicting House Value with Regression Algorithms Chevron down icon Chevron up icon
Predicting User Behavior with Tree-Based Methods Chevron down icon Chevron up icon
Customer Segmentation Using Clustering Algorithms Chevron down icon Chevron up icon
Analyzing Visitor Patterns to Make Recommendations Chevron down icon Chevron up icon
Section 3: Deep Learning Chevron down icon Chevron up icon
Implementing Deep Learning Algorithms Chevron down icon Chevron up icon
Implementing Deep Learning with TensorFlow on AWS Chevron down icon Chevron up icon
Image Classification and Detection with SageMaker Chevron down icon Chevron up icon
Section 4: Integrating Ready-Made AWS Machine Learning Services Chevron down icon Chevron up icon
Working with AWS Comprehend Chevron down icon Chevron up icon
Using AWS Rekognition Chevron down icon Chevron up icon
Building Conversational Interfaces Using AWS Lex Chevron down icon Chevron up icon
Section 5: Optimizing and Deploying Models through AWS Chevron down icon Chevron up icon
Creating Clusters on AWS Chevron down icon Chevron up icon
Optimizing Models in Spark and SageMaker Chevron down icon Chevron up icon
Tuning Clusters for Machine Learning Chevron down icon Chevron up icon
Deploying Models Built in AWS Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(8 Ratings)
5 star 75%
4 star 0%
3 star 12.5%
2 star 0%
1 star 12.5%
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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
Amazon Verified review Amazon
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.
Amazon Verified review Amazon
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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