This training course begins with an introduction to the concepts of Distributed Machine Learning. We'll discuss the reasons as to why and when you should consider training your machine learning model within a distributed environment

Apache Spark
We ll introduce you to Apache Spark and how it can be used to perform machine learning both at scale and speed. Apache Spark is an open-source cluster-computing framework Amazon Elastic Map Reduce We ll introduce you to Amazon s Elastic MapReduce service or EMR for short. EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data. EMR can be easily configured to host Apache Spark

Spark MLlib
We ll introduce you to MLlib which is Spark s machine learning module. We ll discuss how MLlib can be used to perform various machine learning tasks. For this course we'll focus our attention on decision trees as a machine learning method which the MLlib module supports. A decision tree is a type of supervised machine learning algorithm used often for classification problems

AWS Glue
We ll introduce you to AWS Glue. AWS Glue is a fully managed extract, transform, and load service, ETL for short. We ll show you how AWS Glue can be used to prepare our datasets before they are used to train our machine learning models

Demonstration
Finally, we ll show you how to use each of the aforementioned services together to launch an EMR cluster configured and pre-installed with Apache Spark for the purpose of training a machine learning model using a decision tree. This demonstration will provide an end-to end solution that provides machine learning predictive capabilities Intended Audience The intended audience for this course includes:

Data scientists and/or data analysts
Anyone interested in learning and performing distributed machine learning, or machine learning at scale Anyone with an interest in Apache Spark and/or Amazon
Elastic MapReduce

Learning Objectives
By completing this course, you will:

Understand what Distributed machine learning is and what it offers
Understand the benefits of Apache Spark and Elastic MapReduce
Understand Spark MLlib as machine learning framework
Create your own distributed machine learning environment consisting of Apache Spark, MLlib, and Elastic MapReduce
Understand how to use AWS Glue to perform ETL on your datasets in preparation for training a your machine learning model
Know how to operate and execute a Zeppelin notebook resulting in job submission to your Spark cluster
Understand what a machine learning Decision Tree is and how to code one using MLlib

Pre-requisites
The following prerequisites will be both useful and helpful
for this course:

A background in statistics or probability
Basic understanding of data analytics
General development and coding experience
AWS VPC networking and IAM security experience (for the demonstrations)

Course Agenda
The agenda for the remainder of this course is as follows:

We ll discuss what Distributed Machine Learning is and when
and why you might consider using it
We ll review the Apache Spark application, and its MLlib
machine learning module
We ll review the Elastic MapReduce service
We ll provide an understanding what a Decision Tree is
and what types of analytical problems it is suited towards
We ll review the basics of using Apache Zeppelin notebooks
which can be used for interactive machine learning sessions
We ll review AWS Glue. We ll show you how you can use AWS
Glue to perform ETL to prepare our datasets for ingestion
into a machine learning pipeline
Finally - We ll present a demonstration of a fully
functional distributed machine learning environment
implemented using Spark running on top of an EMR cluster