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Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle , Maximo Gurmendez
4.3 (8)
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Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

4.3 (8)
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.
Table of Contents (24 chapters)
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1
Section 1: Machine Learning on AWS
3
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
9
Section 3: Deep Learning
13
Section 4: Integrating Ready-Made AWS Machine Learning Services
17
Section 5: Optimizing and Deploying Models through AWS
Appendix: Getting Started with AWS

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...

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