Machine learning, a subset of artificial intelligence (AI), is the capability of a machine or program to imitate human behavior and perform complex tasks that mimic our ability to solve problems. Java is one of the top programming languages for ML.
Here, we’ll look at the best Java libraries available to help you build machine learning solutions.
One important aspect of machine learning is the four basic approaches, which are:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
On top of selecting the right approach, you’ll also need to know the type of data you want to predict. You can then select the type of algorithm to be used.
In other words, there are a lot of “moving parts” to ML, all of which will be predicated on selecting the right tools.
Fortunately, since Java is a widely accepted language for ML, there are plenty of Java frameworks that can help make the task considerably easier.
But what’s a library? Simply put, a library is a collection of prewritten code that developers can use and reuse to make the development process more efficient and reliable. Almost every programming language has libraries, most of which are open-source and free to use. If you want your teams to work as efficiently as possible, libraries are the way to go. That way, your developers don’t have to reinvent the wheel every time they start a new project.
There are many Java libraries for ML. Because it’s such a prominent programming language, you won’t have any problem finding a Java development company to help build your machine learning projects.
Why Choosing the Right Java Machine Learning Libraries Is Important
Libraries make developing applications considerably more efficient and reliable. Instead of having to write new code for every function or feature, Java developers can make use of various prewritten libraries that have already been vetted and tested. There’s also a lower chance of introducing errors.
Using libraries saves time and money—developers don’t have to solve every problem they face.
Things to Consider When Choosing a Library
Every project, developer, and company will have different needs. Here are some factors to consider:
- ML type: Will your teams use the library or framework for deep learning or a classic machine learning algorithm?
- Language type: Here, we’re looking at Java libraries. However, the project might also require other programming languages. So, you might choose a library that can be used with other languages and/or libraries.
- Scaling: Will you be using this program on an in-house data center or are you developing for the cloud? How far will the project need to scale?
- Data types: You also need to know what data types you’ll be working with. Are your databases SQL or NoSQL? Structured or unstructured data?
- Neural networks: Do you need a library that includes tools for neural network creation?
- APIs: Do you need libraries that include APIs or can interact with other APIs?
- Open-source: Do you need to use a library that’s released with an open-source license or not?
- GPUs: If performance is a top priority, you’ll need to select a library that can work with GPUs.
Having considered the above, what are the best libraries available? Let’s take a look.
The 7 Top Java ML Libraries
Because Java is so popular and works well with ML, as you might have guessed, there are plenty of libraries available. But don’t think you are limited to one library. You might have a larger project that demands multiple libraries.
Weka
If you’re looking for a library that aims to simplify tasks like data mining, Weka is a great option. Weka stands for Waikato Environment for Knowledge Analysis and contains tools for different tasks, such as data classifications, penetration, regression, association rules mining, and clustering.
Weka helps to seamlessly and sustainably store, process, and manage data and can be used anywhere. You can transform stagnant data silos into streaming data pipelines with the simplicity of cloud-native and the performance of an in-house data center cluster. If high performance in the cloud is your top priority, Weka is an outstanding choice.
Weka is used via the Java API, standard terminal applications, or even through a GUI. Use cases for Weka include the following:
- Cloud data storage
- HPC data management
- Data platform for Machine Learning and AI
- Accelerating containerized workloads
Weka is open-source and free to use.
Key Features // Product Highlights
- Weka can preprocess data.
- Weka can assign classes or categories to data items.
- Weka can easily cluster.
- Weka includes support for data association.
- Weka includes a number of select attributes.
- Weka can visualize data.
| PRO | CON |
| Great tool for learning | Limited data analyzing |
| Simple interface | Limited integrations |
| Cluster analysis | |
| Data classification |
DeepLearning4j
DeepLearning4j was created by Eclipse and includes a collection of Java tools geared toward Machine Learning. One of the highlights of DeepLearning4j is that it’s one of the few frameworks that allow you to train Java models while interoperating with Python (which is one of the most popular programming languages for the machine learning model).
The modules in DeepLearnign4j include the following:
- Nd4j – a combination of TensorFlow, PyTorch, and NumPy operations
- Samediff – a low-level framework for complex graph execution
- Python4j – a framework that allows the deployment of Python scripts into a production environment
- Libnd4j – a C++ library to run math code
- Datavec – a library used for data transformation to convert data into tensors that can then be used to run neural networks
- Apache Spark Integration – makes it possible to run deep learning pipelines on Apache Spark
Use cases for DeepLearning4j include importing and retraining models and deploying in JVM microservice environments, mobile devices, IoT, and Apache Spark. This library is one of the best tools for integrating models built in Python.
Key Features // Product Highlights
- Important for Python AI/ML
- Java, Scala, and Python APIs.
- Parallel training through iterative reduction
- Scalable with Hadoop
- Distributed CPU and GPU support
| PROS | CONS |
| Can work with massive data troves | Integrates with Python |
| Works with unstructured data | Integrated with CUDA for GPU access |
| Great for recommendation systems, image recognition, and network intrusion detection |
Apache Mahout
Apache Mahout is an open-source project used to develop ML algorithms and provides for both Java and Scala. This library focuses primarily on common math operations (specifically, linear algebra) and primitive Java collections. Apache Mahout is designed to implement machine learning algorithms very quickly.
Apache Mahout works alongside Apache Hadoop so your teams can apply ML to distributed computing. The core algorithms included with Apache Mahout center around data clustering, mining, and classification.
Key Features // Product Highlights
- Backend agnostic: Apache Mahout abstracts the domain-specific language from the engine where code is processed. This means users can implement any engine required.
- GPU/CPU Accelerators: Apache Mahout improves the speed of the Java Virtual Machine by using “native solvers” that move in-core to offload to either off-heap or GPU memory for faster computing.
- Recommenders: Apache Mahout includes implementations of Alternative Least Squares, Co-Occurrence, and Correlated Co-Occurrence to extend co-occurrence so it can be used on multiple data dimensions.
| PROS | CONS |
| Makes it easier for data scientists to execute algorithms | Can take a considerable time for debugging |
| Free to use | |
| Enables users to roll in additional features |
ADAMS
ADAMS stands for Advanced Data mining And Machine Learning System and is a deep learning library specifically for Java. This library is used to help facilitate the creation of reactive, data-driven workflows and offers a considerable range of operations and actors.
ADAMS is a great option for data mining, retrieval processing, and data visualization. Released on the GPLv3, ADAMS makes it easy to integrate ML into business processes and tightly adheres to the philosophy, less is more. Because of that ADAMS is easy and efficient to use.
ADAMS uses a tree-like structure, in combination with control actors, to define how data flows with zero explicit connections required.
Key Features // Product Highlights
Although ADAMS might not be the most flexible library you’ll ever use, it does have a number of key features, such as the following:
- Includes four types of actors: standalone (no input, no output), source (only output), transformer (input and output), and sink (only input)
- Uses control actors that determine the flow of data or flow execution
- Actors can connect implicitly in a tree structure, as opposed to being placed on a canvas
| PROS | CONS |
| Can work with CI/CD | Requires Java 11 or newer |
| Easy to integrate and start building | Requires Maven 3.8+ |
| Requires TextLive 2010+ |
JavaML
JavaML is a collection of ML and data mining algorithms that includes common interfaces for each. This library is extensible and offers an API for both research scientists and software developers.
Key Features // Product Highlights
- Includes plenty of machine learning algorithms
- Offers common interfaces for each supported algorithm
- Although there is no GUI, developers will find clearly defined, easy-to-use interfaces
- Implementations for algorithms are clearly described in scientific literature
| PROS | CONS |
| Source code is well documented. | Hasn’t been updated since 2012. |
| Tons of available code samples and tutorials. |
JSAT
JSAT is a Java library that makes it easier to get started solving machine learning problems. All of the JSAT code is self-contained with zero external dependencies. JSAT is pure Java and is a solid solution for small- to medium-sized problems. Thanks to support for parallel execution, JSAT is relatively fast.
At the moment, JSAT is being refactored to work with Java 8. Because JSAT is developed by one person, the process is a bit slower than it might be with a team. Since it’s only just migrating to Java 8, there may be some solvable issues.
Key Features // Product Highlights
- JSAT has one of the largest collections of algorithms of any framework.
- JSAT is faster than comparable libraries.
- JSAT is free and open source.
| PROS | CONS |
| Easily integrates into any Java project. | Does not support newer Java releases. |
| Includes algorithms for most ML use cases. |
Apache OpenNLP
Apache OpenNLP is an open-source Java library geared specifically for Natural Language Processing. This library consists of components that include a sentence detector, tokenizer, name finder, document categorizer, parts-of-speech tagger, chunker, and parser.
With Apache OpenNLP, developers can build complete NLP pipelines for all the common NLP tasks, such as sentence segmentation, parts-of-speech tagging, named entity recognition, tokenization, natural language detection, chunking, parsing, and coreference resolution.
Key Features // Product Highlights
- Named Entity Recognition (NER) – Apache OpenNLP supports NER, which makes it possible to extract the names of locations, people, and things.
- Summarize − The summarize feature allows you to summarize paragraphs, articles, documents, and even collections.
| PROS | CONS |
| Very fast development lifecycle | Releases are very slow to become available |
| Outstanding language detection | |
| Dramatically lowers the bar to developing NLP applications |
Conclusion
Java is still one of the most widely used programming languages around. And given how widespread the use of ai development services and machine learning have become, you can bet these technologies will continue to go hand-in-hand into the future. With the right Java machine learning libraries, the sky’s the limit to what your development teams, either internal or outsourced, can do. And as long as they are following Java best practices, the programs they develop can do wonders for your company.
If you enjoyed this, be sure to check out one of our other Java articles:
