Top Java Machine Learning Libraries

Quick Summary:

Java machine learning library empower developers with powerful data analysis, prediction tools, and much more. With some of the best machine learning library for Java, such as Weka, Deeplearning4j, and others, Java developers can seamlessly integrate machine learning features in their applications, enabling them to make intelligent and wise decisions.

Machine learning has evolved as a paradigm-shifting technology reshaping entire sectors and spurring innovation in a wide range of fields. Machine learning is more accessible and important than ever, thanks to data’s exponential expansion and processing capacity improvements. According to Statista, Recent data indicates that the market for machine learning worldwide is anticipated to increase at a CAGR of 18.73% to reach US$528.10bn by 2030. Gartner’s poll from 2021 also showed that 64% of businesses had boosted their spending on AI and machine learning tools.

To harness the potential of machine learning for Java effectively, developers need powerful tools and libraries to simplify the implementation and integration of complex machine learning algorithms into the applications. Nevertheless, whenever we think about machine learning or artificial intelligence, our mind directly goes to Python or R as the programming language. However, what most people need to learn is our good old Java can also be used for the same purpose! Surprised?

In its ecosystem, several machine learning Java libraries provide comprehensive solutions to the issues. These libraries enable Java developers to leverage their existing skills and build intelligent applications that make data-driven decisions, automate complex tasks, and gain valuable insights.

This blog will delve into the best Java machine learning library available for Java. We will also see how these machine learning with Java libraries help the organization use machine learning, as well as why organizations should go for Java when considering applying machine learning. So, whether you are a seasoned Java developer or someone new to the field of machine learning, this blog will serve as a guide to help you navigate the realm of Java machine learning libraries & unlock the potential of machine learning in your application.

Let’s dive in!

But before diving head first into the various options available for Java machine learning libraries. Let’s look at the things to consider before using any library.

Essential Factors and Metrics to Evaluate When Selecting a Machine Learning Library

Before incorporating any Java machine library, it is best to know what things you should remember while selecting any Java libraries for machine learning. Here are some of the factors that you should consider:

Factors to Check When Selecting a ML Library

Type of Machine Learning

Before anything, consider which kind of machine learning tools or libraries your team will use. Is it a framework, or it is the library? Are you going to use a classic Machine Learning algorithm? Or are you going for deep learning?

Type of Language

Here, we’re looking at Java libraries. However, the project might also require other programming languages. Therefore you should choose the library that can be used with other libraries and/or libraries.

Project Scaling

Before selecting any library, you must be clear about whether you will use this project in the in-house data center or will scale it up to the cloud. It is important to be very clear about how much you want to scale.

Type of Datatypes

Along with all this, you also need to be sure which datatypes are you going to use. Are you going to use structured or unstructured? Are you going for SQL or NoSQL databases? Be sure about these questions before selecting any libraries.

API Usage

Does your application need the libraries that come with APIs or can connect with other APIs?

GPU usage

If performance is your top priority, you must select the library that can work with the GPUs.

Also Read: – Top Java Framworks to use

Considering the above, what are the top Java machine learning libraries available? Let’s take a look.

Best Java Machine Learning Libraries & Tools To Use In 2024

Java has become the popular choice of programming language for machine learning. Several machine learning for Java libraries & tools have been developed in recent years, making it easy to develop and deploy machine learning applications in Java. Here is the curated list of the top Java machine learning libraries and tools to watch out for in 2024.

Best Java Machine Learning Libraries & Tools

Apache Spark MLlib

Git Star: 36.2K | Git Fork: 27.1K

The first one on the curated Java Machine learning library list is Apache Spark MLlib. It stands as a distributed and scalable machine learning library embedded within the Apache Spark framework. Equipped with a diverse collection of algorithms for classification, regression, and clustering, MLlib effectively handles extensive data processing. Its seamless integration with Spark’s ecosystem allows developers to construct holistic machine learning pipelines through a high-level API. With multi-language support, MLlib finds widespread employment across industries for tackling large-scale machine learning projects.

Features of Apache Spark MLlib

  • Scalable & distributed computing
  • Wide range of algorithms for classification, regression, and clustering
  • Seamless integration with the Spark ecosystem
  • High-level API and support for building machine learning pipelines
  • Multi-language support for flexibility
  • Extensibility for custom algorithm implementation
  • Widely adopted and proven in industry applications

Features of Apache Spark MLlib


Git Star: 6.4K | Git Fork: 2K

Second, on our curated list of Java machine learning libraries is H20. A distributed in-memory machine learning platform with linear scalability, H2O is entirely open source widespread statistical and machine learning algorithms, such as deep learning, generalized linear models, and gradient-boosted machines, are supported by H2O. Additionally, H2O offers a market-leading AutoML feature that automatically compares all algorithms and their hyperparameters to create a scoreboard of the top models. Over 18,000 businesses use the H2O platform, which is incredibly well-liked in the R and Python communities.

Features of H20

  • Utilizing Leading algorithms, including GLM, GBM, and more.
  • Access to various popular programming languages, including Java.
  • Use of AutoML for automating machine learning workflow.
  • Distributed and In-Memory processing
  • Simple Deployment

Features of H20

Amazon Sagemaker

Third on the list of Java machine learning library is Amazon Sagemaker. Amazon Web Services (AWS) provides Amazon Sagemaker, a fully managed machine learning solution. It offers a full ecosystem for creating, honing, and deploying machine learning models at scale.

SageMaker streamlines the entire machine-learning workflow with a wide variety of built-in algorithms, adaptable model development choices, and easy connectivity with AWS services. It provides secure deployment options, automatic model tuning, and scalable training capabilities to provide effective and secure machine-learning operations.

SageMaker is a well-liked option for enterprises looking for a managed and comprehensive solution for their machine learning needs on the AWS cloud because it enables developers and data scientists to speed up their machine learning projects.

Features of Amazon Sagemaker

  • Completely Managed Services
  • For common ML jobs, built-in algorithms
  • Model Development Flexibility using a few well-known frameworks
  • AWS infrastructure scaling up training
  • Optimizing hyperparameters automatically with a model
  • Real-time and batch prediction deployment options that are flexible
  • Services from AWS are integrated seamlessly
  • Regulations for security and compliance
  • For effective resource use, cost optimization features

Features of Amazon Sagemaker

Massive Online Analysis (MOA)

Next on our list of Java machine learning library is MOA. MOA (Massive Online Analysis) is an open-source framework specifically designed for performing machine learning and data stream mining on the web. It provides a robust environment for implementing and evaluating algorithms that can handle high volumes of streaming data in real time. MOA’s versatility enables researchers and developers to experiment and assess various techniques, including classification, regression, clustering, and ensemble methods. With a strong emphasis on scalability and efficiency, MOA empowers web-based applications to process streaming data and continuously learn in dynamic and ever-evolving online environments.

Features of MOA

  • Open-source
  • Evaluation Framework
  • Incremental Learning
  • Scalability for large volumes of data
  • Variety of Algorithms
  • Stream Data Handling

Features of MOA


The Java Machine Learning Library (Java-ML) is the next in our list of Java Machine Learning libraries. It’s an open-source Java API geared toward software developers, programmers, and computer scientists who want to use Java for machine learning projects. It provides a huge selection of Java machine learning and data mining techniques, such as those for data preprocessing, feature selection, classification, and clustering. Compared to other frameworks, it offers a clear, simple clustering technique. Even though Java-ML lacks a GUI, it still provides a straightforward and widely used user interface. For individuals who are new to using Java to implement machine learning, Java-ML is a fantastic alternative because it offers well-documented source code and a ton of code samples and tutorials.

Features of JavaML

  • An extensive collection of algorithms for data mining and machine learning
  • User-friendly UI that is simple to utilize
  • Execution is simple.
  • Clearly written code
  • Programming language Java compatibility
  • Strong community backing

Features of JavaML


Let’s move on to the next in the list of Java machine learning library – ADAMS. Short for Advanced Data Mining and Machine learning system. The acronym ADAMS stands for Advanced Data Mining And Machine Learning System and adheres to the “less is more” principle. ADAMS is a new and adaptable workflow engine designed to develop and maintain complicated real-world workflows quickly. Under GPLv3, it was made available. As opposed to allowing the user to set operators or “actors” on a canvas and then manually link both input and output, ADAMS leverages a tree-like structure to regulate the flow of data throughout the workflow. There are hence no required explicit connections.

Features of ADAMS

  • Designing machine learning pipelines based on workflow
  • large collection of pre-built modules for machine learning and data mining tasks
  • scaling and assistance with parallel processing
  • Using an interactive GUI, you can create and manage workflows with ease.
  • Integration with well-known machine learning tools and libraries
  • flexibility to modify and broaden processes using user-defined modules
  • automated capabilities for selecting and evaluating models
  • support for streaming and processing real-time data
  • complete workflow and result in logging and monitoring
  • (Java-based) Cross-platform compatibility

Features of ADAMS


Git Star: 745 | Git Fork: 314

Next on our list of Java machine learning library is Elki. It is an open-source data mining program built on Java. The primary goal of ELIK is algorithmic research, with a focus on unsupervised techniques for outlier and cluster analysis. ELKI offers data index structures like the R*-tree, which can yield significant performance advantages, to achieve high performance and scalability.

Researchers and students in this field can easily add new ways to ELKI because it is made to be extensible in this way. ELKI aims to offer a substantial collection of highly parameterizable algorithms to enable quick and equitable benchmarking and evaluation of algorithms.

Features of ELKI

  • Various machine learning and data mining methods are available.
  • Adaptable architecture for algorithm integration and modification
  • Support for a range of index structures to facilitate efficient data processing
  • Scalability and high performance for large datasets
  • The framework that is adaptable for use in research and development
  • Support for a variety of data types, including multidimensional and geographical data
  • Quality and evaluation of algorithms are highlighted
  • Actively supported by the community and open source
  • For convenience of use, there are command-line and programmatic interfaces.
  • Designing modularly for effective experimentation and prototype

Features of ELKI

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Git Star: 764K | Git Fork: 210

Next on the list of the finest Java machine learning libraries is JSAT. Java Statistical Analysis Tools is referred to by the acronym JSAT. JSAT java statistical analysis tool a library for machine learning. To get started with ML quickly, use this Java machine learning package from Github. There are no external dependencies and all of the code in this library, which is made available under the GPL3, is dedicated to education.

It features one of the most comprehensive collections of algorithms of any framework. Since it provides excellent performance and flexibility, it is typically faster than other Java libraries. An object-oriented framework is used to implement almost all of the algorithms individually. It primarily serves specific demands and research.

Features of JSAT

  • An extensive set of indicators for measuring model performance
  • Supports data kinds that are numeric and nominal.
  • Improved performance through effective implementations and optimized algorithms
  • Modular design allows for the extension and customization of algorithms.
  • A combination of Weka and Apache Spark with other Java modules and frameworks.
  • API that is simple to utilize for projects using Java.
  • Updates regularly and active community support.
  • Interoperability between platforms (based on Java).
  • For advice and learning, there is a lot of documentation and examples.

Features of JSAT


Next on the list of Java Machine learning libraries is WEKA. It is the popular pick as a machine learning libraries for Java, used in data mining tasks. Algorithms can be applied directly to a dataset or called from your Java code. It contains tools for functions such as classification, regression, clustering, associating rules, and visualization. Weka machine learning java library is a portable and easy-to-use library that supports time series prediction, feature selection, anomaly detection, and more. It is an acronym for Waikato Environment of Knowledge Analysis. It can be defined as a collection of tools and algorithms for data analysis and predictive modeling along with graphical user interfaces.

Features of WEKA

  • Extensive data preprocessing capabilities
  • A comprehensive set of evaluation metrics for assessing model performance
  • Supports various datatypes
  • GUI for interactive data exploration
  • CLI for automation & scripting
  • Integration with other tools & frameworks
  • Data Visulaization Support
  • Robust Community Support

Features of WEKA


Last but not least on our list of Java libraries for machine learning is Rapid Miner. It is a cutting-edge data science platform, that harnesses the power of Java, a versatile programming language, to deliver a dynamic and scalable solution for the digital era. With its Java foundation, RapidMiner ensures seamless compatibility across diverse operating systems and offers enhanced interoperability with various web technologies. Leveraging the flexibility and robustness of Java, RapidMiner empowers users on the internet to efficiently perform data mining, machine learning, and predictive analytics tasks, enabling them to extract valuable insights from complex datasets with ease.

Features of RapidMiner

  • Straightforward visual interface
  • Large collection of built-in operators
  • Competences for advanced analytics
  • Thorough data transformation and preparation
  • Integration and support for numerous data formats
  • Model evaluation and selection automatically
  • Integration with widely used frameworks and programming languages
  • Optimizations for scalability and performance
  • Options for production deployment
  • Community involvement and frequent updates

Features of RapidMiner 

Now that you have an understanding of Java machine learning libraries and their features, let’s understand how tech giants use machine learning in Java for their ecosystem to integrate and use this open-source machine learning library with Java.

Also Read: – Top Java Development Tools to utilize

How do Well-Known Companies use Machine Learning in Java?

Java has established itself as the most widely used programming language for machine learning and continues to grow in popularity. Given the qualities like adaptability, scalability, performance, and mobility, it is simple to understand why. In addition to having excellent Machine Learning functionality, Java has received significant support from some of the world’s largest corporations. Let’s see how these tech giants use machine learning in their ecosystem.

Companies use Machine Learning in Java


Google has invested significant efforts in developing machine learning frameworks like TensorFlow in Python, but Java also plays a crucial role in their ecosystem. Google leverages Java extensively in executing machine learning workloads on the Cloud Machine Learning Engine (CMLE) within the Google Cloud Platform. The platform offers a range of cutting-edge ML algorithms accessible through both a web-based interface and the native Java API. Additionally, Java is utilized in supporting various Google initiatives such as Natural Language Processing using Cloud Speech-to-Text and Image Recognition with Cloud Vision APIs.


Netflix doesn’t need any introduction, but you would be surprised that it also relies on Java for its extensive utilization of Machine Learning. Java serves as the foundation for Netflix’s ML framework, enabling personalized recommendations by analyzing customers’ viewing history. Employing Apache Spark, Kafka Streams, and Java 8, Netflix efficiently processes vast real-time streaming data. Their Machine Learning algorithms, implemented in Java, are deployed on cloud platforms to expedite training processes.


The usage of ML algorithms that are primarily powered by the Java codebase is familiar, with LinkedIn being another one on the list. LinkedIn mostly employs ML models for personnel recommendation, such as suggesting suitable employees based on job type or offering new opportunities that are best suited to an individual’s skill set based on past employment history and search patterns. The LinkedIn development team uses the Apache Mahout machine learning library. They can rapidly, and with little effort from the developers, create robust ML algorithms that are written in pure Java code.

IBM  Watson

IBM Watson is a well-known IBM artificial intelligence platform mostly powered by Java code. Unlike any other ML technology on the market, this enables developers to create complex ML models more accurately. Using clean Java code and information made available by IBM’s cloud platform Bluemix Services, Watson employs deep neural networks. The input is then processed by the ML model using methods for natural language processing to yield insights that can be applied to business choices.

Overall, it is obvious why so many large corporations rely heavily on Java for Machine Learning-related tasks; Java’s scalability, performance, flexibility, and portability make it the perfect option for businesses looking for effective Machine Learning solutions without having to worry about maintenance costs or compatibility issues between different parts of their systems. Additionally, compared to other languages like Python, the development of complicated ML models becomes considerably simpler due to the vast array of libraries specifically created for Java-based ML applications. Now that you have seen how tech giants use Java machine learning libraries in their ecosystem let’s see why businesses choose Java for machine learning.

Why Business Choose Java for Machine Learning?

Following are some key reasons businesses choose Java for machine learning. They are as follows:

Why Business Choose Java for Machine Learning

  • Java is a programming language that has been widely used and developed and has a vibrant community.
  • It has the availability of a wealth of libraries, frameworks, and resources designed specifically for machine learning.
  • Java can handle massive datasets and sophisticated models with excellent scalability and performance.
  • Java has multi-threading capability for high-performance computing, and effective memory management is essential.
  • Java offers platform independence, enabling deployment across various platforms, such as embedded systems and the cloud.
  • Thanks to its robustness and security features, it is a dependable option for enterprise applications.
  • Java has the capabilities for easy integration with current technologies and systems.
  • There are Java-specific machine learning frameworks and libraries available.

Wrapping Up!

In a nutshell, Java machine learning libraries stand as a vital and dynamic component of the web development landscape. With its extensive range of frameworks and libraries, Java empowers developers to harness the power of machine learning algorithms, process large volumes of data, and seamlessly integrate with other Java-based web technologies. The scalability, performance, and platform independence of Java make it an ideal choice for building intelligent web applications that leverage machine learning capabilities. By utilizing Java machine learning libraries, businesses can unlock new possibilities for data-driven decision-making, personalization, recommendation systems, and other web-based machine learning applications, ultimately enhancing user experiences and driving innovation in the online domain.

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    Saurabh Barot

    Saurabh Barot, the CTO of Aglowid IT Solutions, leads a team of 50+ IT experts across various domains. He excels in web, mobile, IoT, AI/ML, and emerging tech. Saurabh's technical prowess is underscored by his contributions to Agile, Scrum, and Sprint-based milestones. His guidance as a CTO ensures remote teams achieve project success with precision and technical excellence.

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