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Mastering Scala Machine Learning

Mastering Scala Machine Learning

By : Kozlov
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Mastering Scala Machine Learning

Mastering Scala Machine Learning

By: 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 (12 chapters)
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10
10. Advanced Model Monitoring
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11
Index

Chapter 10. Advanced Model Monitoring

Even though this is the last chapter of the book, it can hardly be an afterthought even though monitoring in general often is in practical situations, quite unfortunately. Monitoring is a vital deployment component for any long execution cycle component and thus is part of the finished product. Monitoring can significantly enhance product experience and define future success as it improves problem diagnostic and is essential to determine the improvement path.

One of the primary rules of successful software engineering is to create systems as if they were targeted for personal use when possible, which fully applies to monitoring, diagnostic, and debugging—quite hapless name for fixing existing issues in software products. Diagnostic and debugging of complex systems, particularly distributed systems, is hard, as the events often can be arbitrary interleaved and program executions subject to race conditions. While there is a lot of research...

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Mastering Scala Machine Learning
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