Expanded Library

Scalable Machine Learning with the Microsoft Machine Learning Server

by Shawn Hainsworth

In this course, you will learn how to create big data machine learning experiments using the Microsoft Machine Learning Server. Detailed code examples in both R and Python demonstrate how to scale your code and work with Apache Spark and SQL Server.

What you'll learn

Working with big data often exceeds the capacity of in-memory dataframes. In this course, Scalable Machine Learning with the Machine Learning Server, you will learn how to build scalable, end-to-end machine learning experiments using both R and Python using the Microsoft Machine Learning Server. First, you will learn how to import, process, transform, and visualize big data. Next, we will cover how to write custom, scalable, distributed functions which can be executed in a number of compute contexts. In addition, you will learn how to use the state of the art machine learning algorithms included in the MicrosoftML package. Then, we will integrate machine learning experiments into SQL Server. Finally, we will cover how to using the machine learning server with Hadoop and Spark, including integration with popular frameworks such as PySpark, SparkR and Sparklyr. We will spin up an HDInsight cluster in Microsoft Azure, and also build a Spark development environment from scracth. When you’re finished with this course, you will have the skills and knowledge needed to build scalable machine learning experiments using R and Python using XDF files, the Hadoop Distributed File System, SQL Server and Apache Spark.

Table of contents

Course Overview

About the author

Shawn has more than twenty-five years of experience as an architect and developer. Since 2011, Shawn has focused on business intelligence and machine learning. He has experience in Java, C#, R, Python, Hadoop, Mongo, Kafka, and Storm. He is a presenter at technology conferences and blogs as "The Legal BI Guy."

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