Top 10 Java Machine Learning Tools and Libraries

Virtual personal assistants and other progressive technologies rely on advances in Artificial Intelligence. The most popular AI fields are natural language processing, machine learning, and deep learning. Big companies employ them in activities ranging from online advertising targeting to self-driving cars. Consequently, ML experts are in demand, and ML and deep learning are some of the hottest skills currently. The number of tools that simplify programmers’ work is growing too.

 

Onix AI and ML teams have extensive expertise in building innovative functions and products employing these technologies. So, we decided to share the top 10 Java ML libraries businesses turn to their advantage. This article is intended not only for Java web developers. Business owners need to know whether a programmer can develop ML applications efficiently, which includes familiarity with machine learning packages in Java. Moreover, if you have a say in the tech stack discussions, it’s helpful to know the context.

The focus on Java machine learning reflects the popularity of the language. Due to its extreme stability, leading organizations and enterprises have been adopting Java for decades. It’s widely used in mobile app development for Android, which serves billions of users worldwide.

 

 

For implementing machine learning algorithms Java developers can utilize various tools and libraries. At least 90 Java-based ML projects are listed on MLOSS.org alone. This article features ten most commonly used libraries and platforms; it briefly describes the kind of problems they can solve and the algorithms they support.

 

Top Java Machine Learning Libraries & Tools

Java has become one of the most popular programming languages for Machine Learning. In recent years, several Java libraries and tools have been created to make developing and deploying ML applications in Java easier. Thus, here is a list of the top Java Machine Learning Libraries and tools to watch out for in 2023 and beyond.

 

TensorFlow Serving

TensorFlow Serving is an open-source library for serving machine learning models focusing on low latency performance. It can run locally or in the cloud and supports many different models, from deep convolutional networks to linear models. It enables developers to quickly deploy ML models at scale without having to manage infrastructure manually.

 

Apache Spark MLlib

Apache Spark MLlib: Apache Spark MLlib is a library designed specifically for building machine learning pipelines on Apache Spark clusters. It provides high-level APIs that allow developers to quickly create robust machine learning pipelines using distributed data training algorithms and other distributed processing tasks. Additionally, its frame ml library allows developers to access components like feature transformers or evaluation metrics as reusable objects in their pipelines. 

 

 

Deeplearning4j

Deeplearning4j (DL4J) is a comprehensive deep learning library built on top of Java Virtual Machine (JVM). It helps developers build production-ready applications with support for GPU acceleration, distributed computing, and various neural network architectures including convolutional nets, recurrent neural nets, LSTM networks, and more. DL4J also provides a GUI-based user interface for Hyperparameter Tuning that makes it easy to optimize model performance. 

 

H2O

H2O is an open-source machine learning platform designed specifically for big data analytics with support for Python, R, and other programming languages, including Java. With its automatic machine learning (AutoML) capabilities, H2O makes it easy to train supervised and unsupervised models without writing any code at all - just point it at your data set and let it do its magic! On top of this, H2O also offers advanced features such as grid search, hyperparameter tuning, graphical model selection tools, automated feature engineering tools, and much more!  

 

Amazon Sagemaker

Amazon Sagemaker is a managed service offered by Amazon Web Services that simplifies the process of deploying ML applications into production environments with minimal effort. It supports many different frameworks, including TensorFlow, Keras, and custom algorithms written in Java through its SDKs. It is useful if you want to use existing Java libraries in your application or even create your own custom algorithm from scratch.

 

Matplotlib

Matplotlib is an open-source plotting library used extensively by data scientists working with Python but can be adapted quite easily into Java programs too! Users can easily visualize their results by creating custom plots such as scatter plots or histograms directly from their Java application(s). This makes it very handy when trying to understand what’s going on under the hood while developing machine learning applications in Java without having to learn another language/toolset! 

 

 

JavaML

The JavaML library provides an API layer between traditional numerical computation libraries such as Apache Commons Math Library & JAMA Linear Algebra Library, as well as popular Machine Learning Libraries such as Weka & MOA, allowing users to simplify complex calculations whilst adding an additional level of abstraction & flexibility when developing new algorithms & experiments over large datasets quickly & efficiently. With all these great options available today, it’s clear that there are plenty of possibilities available when developing machine learning solutions using Java – whether you’re looking for something lightweight & simple like matplotlib or something more powerful like Amazon Sagemaker – you’ll be sure to find what you need!

 

MOA

MOA Java ML is a leading machine learning library for Java developers. The library allows them to use powerful algorithms and tools to create and apply machine learning models. It also provides an extensive set of APIs and functions to enable developers to quickly and easily build real-world applications. This library has been designed to make machine learning development in Java as easy and intuitive as possible. It also includes tools to help developers analyze data, build models, and deploy them to production.

 

Weka

Weka is a comprehensive Java ML library that enables users to efficiently perform various tasks such as data preprocessing, classification, clustering, regression, and feature selection. It contains multiple advanced algorithms such as Bayesian networks, naïve Bayes classifiers, and support vector machines (SVMs). 

 

In addition to these algorithms, Weka provides a graphical user interface (GUI) for easy data visualization of datasets and their accompanying results. This makes it possible for users to quickly analyze datasets without having extensive knowledge of machine learning theory or coding expertise. In 2023, Weka will remain an essential tool for developers looking to create powerful ML applications easily. Its vast selection of tools makes it well-suited for novice and experienced developers. 

 

 

Stanford CoreNLP

Stanford CoreNLP Java ML will be an invaluable tool for developers and machine learning professionals in 2023 and beyond. This powerful library is used for natural language processing and text mining. It features various components such as sentiment analysis, machine translation, coreference resolution, and part-of-speech tagging. It's also designed for scalability, allowing users to process complex applications easily. Moreover, Stanford CoreNLP Java ML can be used to develop custom NLP models and applications that work with any other Java library.

 

Accord.NET

Accord.NET is a powerful library that provides linear algebra, machine learning algorithms, and other tools to develop machine learning applications. It also contains a wide variety of components, including support vector machines, neural networks, and decision tree algorithms.

 

Why Do Businesses Choose Java for Machine Learning?

 

 

 

Netflix is another well-known company that extensively uses Machine Learning and relies heavily on Java for this purpose. Netflix uses several ML models to provide personalized recommendations to its customers based on their past watch history. Netflix’s ML framework uses a combination of Apache Spark, Kafka Streams, and Java 8 to handle large amounts of data from streaming services in real-time. The Machine Learning algorithms are written in pure Java code and deployed onto a cloud platform for faster training times.

 

Java is an excellent choice for machine learning projects. It is an efficient language that can help businesses create programs to analyze data and make predictions. Java makes developing, maintaining, and modifying machine learning programs easier. With Java, developers can create scalable programs with minimum code and effort. It also provides simple language-level support for concurrent programming.

 

In addition, with ML in Java, businesses can quickly build machine learning solutions without sacrificing accuracy or performance. In addition to providing the tools to develop powerful applications quickly, it is also easy to maintain and modify ml programs written in Java. It also offers a wide range of machine learning libraries that developers can use to implement ml algorithms into their programs quickly.

 

Overall, the ease of use and powerful features make Java an excellent choice for machine learning projects. Java is perfect for businesses looking to implement machine learning into their operations.

 

How Do Famous Companies Use Java Machine Learning?

Java is one of the most popular programming languages used for machine learning and is becoming increasingly more popular daily. When considering its scalability, performance, flexibility, and portability, it's easy to see why. Not only does Java offer great Machine Learning features, but major companies have invested heavily in using Java to solve their ML problems.

 

For instance, Google has been actively developing its own Machine Learning frameworks, such as TensorFlow in Python, but it also uses Java extensively. Google’s Cloud Machine Learning Engine (CMLE) provides managed services for running ML workloads on the Google Cloud Platform. Google Cloud Platform includes many advanced ML algorithms that can be used via the native Java API or through its web-based interface. Other Google projects, like Natural Language Processing with Cloud Speech-to-Text and Image Recognition with Cloud Vision APIs, are also powered by Java code behind the scenes.

 

Netflix is another well-known company that extensively uses Machine Learning and relies heavily on Java for this purpose. Netflix uses several ML models to provide personalized recommendations to its customers based on their past watch history. Netflix’s ML framework uses a combination of Apache Spark, Kafka Streams, and Java 8 to handle large amounts of data from streaming services in real-time. The Machine Learning algorithms are written in pure Java code and deployed onto a cloud platform for faster training times.

 

Moreover, IBM Watson is a renowned Artificial Intelligence platform developed by IBM which uses ML algorithms powered mainly by Java code. This technology allows developers to build sophisticated ML models with higher accuracy than any existing ML technology available today. Watson uses deep neural networks written in clean Java code, which are trained using datasets provided by IBM’s cloud platform Bluemix Services. The ML model then processes the data using natural language processing techniques to generate insights from the data, which can be used to develop business decisions.

 

LinkedIn is one more company that uses ML algorithms powered mainly by Java codebase. LinkedIn uses ML models mainly for personnel recommendation purposes, such as suggesting potential employees based on job type or recommending new roles that best fit an individual’s skill set based on the past job experience and search histories. LinkedIn developers use the open-source ML library Apache Mahout. It allows them to quickly implement powerful ML algorithms written in pure Java code with minimal effort required from the developers.

 

All in all, it is clear why many big companies rely heavily on Java for Machine Learning related tasks; its scalability, performance, flexibility, and portability make it the ideal choice for companies looking for efficient Machine Learning solutions without having to worry about maintenance costs or compatibility issues between different components of their systems. Furthermore, given the huge range of libraries explicitly built for Java-based ML applications, developing complex ML models becomes much easier compared to other languages such as Python.

 

 

TL;DR

 

This article lists ten popular Java AI frameworks, most of them open source. The choice of a framework mainly depends upon the support for algorithms and the implementation of neural networks. Speed, dataset size, and ease of use are other factors that often affect decision-making. What’s most important when choosing a Java machine learning library is to understand your project requirements and the problems you intend to solve.

 

Apache Spark’s MLib is also known to be robust and fast when it comes to the processing of large-scale data. Deeplearning4j is considered one of the best because it takes advantage of the latest distributed computing frameworks to accelerate training. Mahout offers high performance, flexibility, and scalability.

 

Weka is probably the best Java ML library out there. The vast collection of algorithms and tools for data analysis and predictive modeling has implementations of most ML algorithms. Related to the Weka project, MOA performs big data stream mining in real-time and large-scale ML. MOA aims for time- and memory-efficient processing. Compared to Weka, Java-ML offers more consistent interfaces. It has an extensive set of state-of-the-art similarity measures and feature-selection techniques. There are implementations of novel algorithms that are not present in other packages. Java-ML also features several Weka bridges to access Weka’s algorithms directly through the Java-ML API.

 

Although non-exhaustive, the list of Java machine learning libraries hopefully will be useful when you are about to design, build, and deploy an ML application. Contact us if you need professional help!

 

 

FAQ

 

Is Java good for machine learning?

When discussing the use of Java for machine learning, it's clear that there are both advantages and disadvantages to consider. On one hand, Java is a versatile language with robust support for object-oriented programming, making it well suited for tackling complicated tasks like developing machine learning applications. On the other hand, certain aspects of the language can make it difficult to write efficient code and integrate it with existing ML libraries. Ultimately, whether or not Java is good for machine learning depends on the specific needs of the project.

 

What is the role of deep learning in Java?

Deep learning is an area of artificial intelligence that focuses on teaching computers to recognize complex patterns and understand large amounts of data. Java is a great option for developing deep learning applications, as the language offers a number of powerful libraries and frameworks for building machine learning models. Additionally, many existing deep learning algorithms have been implemented in Java, allowing developers to quickly create sophisticated applications without having to write code from scratch. Therefore, the role of deep learning in Java is to make the development of machine learning applications easier and more efficient.

 

How long does it take, on average, to build an ML solution using Java?

The average time required to build a machine learning solution using Java depends on the complexity of the task and the familiarity of the developer with the language. Generally, simple tasks can take a few weeks while more complex projects can take months. Additionally, many existing libraries and frameworks provide pre-built models that can speed up development time significantly. Thus, it is difficult to provide an exact timeline for building an ML solution using Java as there are many variables that need to be taken into consideration.