Mastering Machine Learning on AWS

This is the code repository for Mastering Machine Learning on AWS, published by Packt.

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

What is this book about?

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.

This book covers the following exciting features:

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

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

vectorizer = CountVectorizer(input=dem_text + gop_text,
stop_words=stop_words,
max_features=1200)

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

Software and Hardware List

Chapter	Software required	OS required
All	Python 3.6 or higher	Windows, Mac OS X, and Linux (Any)
All	Jupyter notebook	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Authors

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.

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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