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.
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.
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.
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.
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.
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
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
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)
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
If you have thoughts or suggestions for this course, please contact Cloud Academy at firstname.lastname@example.org.
- [Instructor] Welcome back. In this lecture we'll introduce you to MLlib. MLlib is Apache Spark's scalable machine learning library. Spark MLlib enhances machine learning because of it's simplicity, scalability and easy integration with other tools in the Spark ecosystem. Apache Spark comes with a native machine learning library, MLlib that is designed for simplicity, scalability and easy integration with other Spark tools.
With the scalability, language compatibility and speed of Spark, data scientists can solve and iterate 3D data problems faster. Spark MLlib can be used for a number of common business use cases and can be applied to many datasets to perform feature extraction, transformation, classification, regression and clustering amongst other things as well.
Some example business use cases where MLlib excels are advertising and marketing optimization, anomaly and fraud detection, recommendation systems. Spark MLlib implements many common machine learning algorithms. Depending on your problem type, whether it be binary classification, multi-class classification, or regression, then you can leverage any of the supported algorithms shown here. Later on in our course when we build an end-to-end demonstration, using Apache Spark and MLlib we'll train our machine learning model using the decision tree algorithm.
Let's take a quick look at a couple of MLlib code examples. For starters, we will use Scala as the language of choice for both examples. Note, we can also author the same code in either Java or Python. In this particular example, we first load the training data, we then create a data frame containing our labels and features.
Next, we create and initialize a logistic regression model. Finally, we train the model and then show the results. In the next example, we train a decision tree model using a 70/30 split for the training and test data. We then test the model for its accuracy in terms of making correct predictions. Finally, we print out the learning decision tree model.
Okay, that concludes our basic introduction to Apache MLlib and the machine learning capabilities it provides. As you've just seen, MLlib supports training decision tree models which will be the focus of our next lecture. Go ahead and close this lecture and we'll see you shortly in the next one.
Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.
He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.
Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).