Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

System monitoring


While there are other types of monitoring dealing specifically with ML-targeted tasks, such as monitoring the performance of the models, let me start with basic system monitoring. Traditionally, system monitoring is a subject of operating system maintenance, but it is becoming a vital component of any complex application, specifically running over a set of distributed workstations. The primary components of the OS are CPU, disk, memory, network, and energy on battery-powered machines. The traditional OS-like tools for monitoring system performance are provided in the following table. We limit them to Linux tools as this is the platform for most Scala applications, even though other OS vendors provide OS monitoring tools such as Activity Monitor. As Scala runs in Java JVM, I also added Java-specific monitoring tools that are specific to JVMs:

Area

Programs

Comments

CPU

htop, top, sar-u

top has been the most often used performance diagnostic tool, as CPU and memory...