Java is a ubiquitous programming language that is capable of running on almost any digital device. Since its launch in 1996, it continues to be one of the top 3 languages on GitHub. Java's popularity as a development language stems from its multi-platform support, object-oriented programming alignment, and its ability to run on any device from a traditional PC or server to a modern IoT device or mobile phone.

When developers think about writing machine learning solutions, languages like Python or R come to mind. However, Java is a viable alternative for a variety of reasons. Not only does its prevalence across all devices make it easy to connect to various endpoints, but it also has many other benefits that make it an ideal candidate.

Machine Learning and Big Data

With the exponential rise of cloud computing services came the dawn of big data and machine learning. Although these two concepts have nuances regarding their particular applications, they both rely on vast quantities of data. Machine learning and big data both need extensive data sets to improve their accuracy and reliability.

As the world embraced cloud computing, it created an ecosystem to generate more data than had ever been seen before. In 2019, the IORG reported that the world produced 90% of online data in the preceding two years alone. Machine learning and big data concepts may have been around before the rise of the cloud. However, they needed the extensive data set the cloud provides to improve the accuracy of their algorithms.

If you look at some of the popular data science frameworks and tools such as Hadoop, Spark, Hive, and Flink, they are all written in Java. Since they have a Java platform and foundation, leveraging this programing language for machine learning offers better integration potential.

Java's Track Record

Since its inception, Java has always been a popular programming language for enterprise software development. Its scalability, multi-device support, and object-oriented alignment have proven Java's capability as a software solutions platform. As a result of its proven track record, Java remains a prevalent technology in enterprise environments. These qualities make Java a good contender as a potential platform for machine learning solutions.

As Java is the core software technology running many enterprise systems, it will be far easier to integrate solutions or extract data for machine learning requirements. The popularity of Java also means that the market offers an ample supply of suitably qualified and experienced developers. Consequently, it will be far easier to find Java resources when you need to staff your next machine learning project.

Java for Machine Learning

Over and above the popularity and widespread utilization of Java in enterprise software environments, data scientists have leveraged Java to build machine learning components. If you consider the science behind the technology, machine learning relies on several key processes. Cleaning data, importing and exporting it, statistical analysis, Natural Language Processing (NLP), deep learning, and data visualization are all core functions in a machine learning solution. Several Java tools and frameworks already exist that leverage this software programming language for critical machine learning functions.

Deep Learning for Java

Deeplearning4j (DLJ4) is an open-source, distributed, deep-learning library written in Java that runs on the Java Virtual Machine (JVM). It takes advantage of distributed computing frameworks, including Apache Spark and Hadoop. As it leverages the Java Virtual Machine, DL4J is compatible with other JVM-capable languages such as Scala, Clojure, and Kotlin. It provides an API so you can create your own neural networks and supports several structures, including feedforward neural networks, convolutional neural nets, and deep belief networks.

ELKI

ELKI is an open-source data mining framework written in Java. Its focus is research in algorithms and can help developers with unsupervised methods in cluster analysis. It is also helpful in building solutions for outlier detection. Its differentiator is that it separates data mining algorithms and data management tasks allowing for independent evaluation.

Java-ML

Java Machine Learning Library (Java-ML) is another machine learning tool written in Java. It offers a collection of machine learning algorithms with a standard interface for each type. Its primary users are software engineers and programmers, and it comes with well-documented source code, code samples, and tutorials.

JSAT

The Java Statistical Analysis Tool (JSAT) is a library written in Java that helps software developers rapidly get started with machine learning problems. It is pure Java and has no external dependencies and offers one of the largest collections of algorithms available. JSAT’s ideal usage scenario is small and medium-size problems.

WEKA

WEKA is another open-source machine learning tool written in Java. You can access it via a graphical user interface, standard terminal applications, or its Java API. Used for teaching, research, and industrial applications, it comes with an abundant supply of built-in tools for everyday machine learning tasks. It also offers transparent access to other toolboxes such as scikit-learn, R, and DL4J.

Additional Java Advantages for Machine Learning

In addition to its track record and presence throughout enterprise software, Java has other benefits that make it a well-suited development platform for machine learning. It is a strongly typed programming language that ensures developers are explicit and specific about variables and data types. This strongly-typed feature simplifies the management of large data applications while making it easier to maintain the codebase.

Java is also faster than other machine learning programming languages. Its inherent scalability makes it an excellent choice for building more extensive and complex machine learning applications. As it leverages the Java Virtual Machine, it enables software developers to write code for multiple platforms and create custom tools for any machine learning solution.

The Versatility of Java

Java is, and always has been, a versatile software development language. Built to support any device on any platform, it remains an invisible force behind many of the devices and apps we use every day. It also powers enterprise software and has features that align it perfectly with large, complex software development solutions.

When it comes to machine learning, Java is more than capable of handling any project or problem. Many machine learning libraries, tools, and big data platforms support this programming language. The fact that many have Java as their core development language talks to this software development platform's benefits as a machine learning solution.

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