Big Data, AI and Machine Learning

Big Data, AI and Machine Learning

Big Data is a data collection characterised by huge volumes, rapid velocity, and great variety. The magnitude of the huge volumes can be characterised by saying that it is typically measured in petabytes (PB), where one PB equals one million gigabytes (GB). That is truly a huge amount of data. Rapid velocity refers to how rapidly the data is generated or created. So how does AI affect Big Data? The answer is that an AI learning system when applied to a Big Data set allows users to extract useful information from a huge and noisy input. Typical computer systems that can handle Big Data are composed of thousands of processors working together in a parallel fashion to speed up the data reduction process, often referred to as MapReduce.

Machine Learning

Java programming language: This is a widely used language, and the libraries are well supported. Java is not the only language that is used for machine learning—far from it. If you are working for an existing organisation, you may be restricted to the languages used within it. With most languages, there is a lot of crossover in functionality. With the languages, that access the Java Virtual Machine (JVM) there is a good chance that you will be accessing Java-based libraries. There is no such thing as one language being “better” than another is. It is a case of picking the right tool for the job.

Python: The Python language has increased in usage because it is easy to learn and easy to read. It also has some good machine learning libraries, such as scikit-learn, PyML, and pybrain. Jython was developed as a Python interpreter for the JVM, which may be worth investigating.

R: R is an open source statistical programming language. Whilst the syntax is not the easiest to learn, it is well worth evaluating. It also has a large number of machine learning packages and visualisation tools. The RJava project allows Java programmers to access R functions from Java code.

Matlab: The Matlab language is widely used within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualisations and graphs.

Scala: A new breed of languages is emerging that takes advantage of Java’s runtime environment, which potentially increases performance, based on the threading architecture of the platform. Scala (which is an acronym for Scalable Language) is one of these, and it is being widely used by a number of start-ups. There are machine-learning libraries, such as ScalaNLP, but Scala can access Java jar files, and it can also implement the likes of Classifier4J and Mahout.

Clojure: Clojure, another JVM-based language, is based on the Lisp programming language. It is designed for concurrency, which makes it a great candidate for machine learning applications on large sets of data.

Ruby: Many people have heard about the Ruby language by association with the Ruby On Rails web development framework, but it is also used as a standalone language. The best way to integrate machine-learning frameworks is to look at JRuby, which is a JVM-based alternative that enables you to access the Java machine learning libraries.

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