In 5 years, the machine learning (ML) market size is projected to top $31 billion. This growth is mainly due to the advancements we’re seeing in AI, but right behind that is the increasing need companies have to reduce costs and streamline processes. Machine learning at its most basic level is a data management tool that retains information and improves from experience which is something every company wants from employees. The difference now is that it’s scalable and has less margin for error in addition to its work capacity which continues to improve over time. Managing data is among the highest in-demand skills globally for businesses right now.
According to a report from Cision, the 10-year period up to 2030 will double the market size of global enterprise data management. What this means, in a broad sense, is that companies across various industries, investors, and especially tech leaders are recognizing the value of effectively managing and utilizing data. Ultimately they know something others don’t, or they’re just accepting something others won't which is that data holds the most potential for business growth in the future. They’re putting their money where their mouth is by investing in and utilizing systems that leverage machine learning and ultimately make this process more accessible.
Where Java Comes In
Java, being the versatile programming language it is, offers tons of libraries and frameworks that facilitate the development of machine learning. These libraries have pre-built algorithms and tools that simplify the implementation of machine learning models and make the development process way more efficient. In this blog, we’re looking at some of the top Java libraries for machine learning that can help developers to leverage it effectively in their applications.
Before we get to that, here’s what you want to be thinking about when selecting a Java machine learning library:
Algorithm support: Assess the library's support for different machine learning algorithms, like linear regression, decision trees, support vector machines, and of course neural networks.
Ease of use and improvement: Look for libraries that offer easy-to-use APIs and utilities for training machine learning models. Consider the availability of tools for cross-validation, hyperparameter tuning, and model evaluation.
Feature engineering and data preprocessing: Does the library have functionalities for feature extraction, transformation, and normalization? Look for utilities that simplify common data preprocessing tasks, such as handling missing values, categorical encoding, and feature scaling.
Support for big data processing: This is a big one. If you're working with large-scale datasets, you’ll likely want libraries that seamlessly integrate with distributed computing frameworks like Apache Spark.
Visualization and interpretation: Check if the library offers tools for visualizing data, or interpretability. Visualizations are going to help with understanding your data and model behaviour, while interpretability tools help you gain insights into the factors driving your model's predictions.
Deployment and integration: Evaluate how easily the library can be integrated into your existing software stack and deployed in production environments. Look for libraries that offer options such as model import/export or support for common deployment frameworks like TensorFlow Serving or Apache Kafka.
Performance optimizations: Consider libraries that offer optimizations like parallel computing, GPU acceleration, or distributed training.
There’s a lot to consider when choosing an ideal framework, these variables will help guide your choice but ultimately your unique variables will be the key factors such as project requirements, team expertise, and the overall goals you want to achieve.
With that, here are some of the top options that Java offers for ML libraries:
1) Deeplearning4j (DL4J):
DL4J is a Java library that specializes in deep learning. It provides sets of tools and algorithms for building and training deep neural networks. With its integration with Apache Spark and Hadoop, DL4J enables distributed deep learning on big data platforms. It also supports various neural network architectures, such as convolutional networks (CNNs) and recurrent networks (RNNs).
2) Smile:
Smile, or Statistical Machine Intelligence and Learning Engine, specializes in a range of AI tasks. When it comes to machine learning model integration and data analysis, Smiles's interface is user-friendly and has a ton of algorithms for classification, regression, clustering, dimensionality reduction, and so on.
3) Weka:
Weka, an open-source Java library, has been a go-to among machine-learning enthusiasts for many years. It offers a vast collection of machine learning algorithms and tools for data preprocessing, classification, regression, clustering, and especially association rule mining.
Weka's graphical user interface, called the Weka Explorer, lets users try out different algorithms. It also provides extensive support for data visualization, which makes it easier to understand and interpret the patterns in the data.
4) MOA:
Massive Online Analysis (MOA) is an open-source Java framework designed specifically for online learning and mining big data streams. It offers a variety of machine-learning algorithms that can handle consistent data streams in real-time. For developers, MOA allows them to build scalable and efficient models that adapt to changes in data over time.
Like the last two, it also includes algorithms for classification, regression, clustering, and additionally anomaly detection. MOA's focus on online learning makes it a great tool for applications where data arrives continuously and needs to be processed immediately.
5) DL-Learner:
DL-Learner focuses on machine learning with description logic (DL). It specializes in knowledge extraction from structured data and supports creating logical knowledge bases. DL-Learner includes algorithms for ontology learning, rule induction, and concept learning. It can be used to build intelligent systems that not only extract knowledge from data but also reason with logical rules.
DL-Learner is particularly useful in domains where say for instance formal representation and reasoning are essential, so things like semantic web applications and knowledge-based systems for instance.
6) Apache Mahout:
Apache Mahout is a scalable machine learning library that has algorithms for the typical clustering and classification, but also recommendation mining. It integrates with big data platforms like Apache Hadoop and Apache Spark, which allows developers to leverage a more distributed computing landscape.
Apache Mahout supports various machine-learning techniques, including collaborative filtering, clustering, and classification. It’s suitable for large-scale data analysis which is why it’s widely used in industries like e-commerce, social media, and anything that leverages personalized recommendations.
7) ADAMS:
Advanced Data mining And Machine Learning System (ADAMS), is a data-driven workflow engine, and an open-source, modular framework. When it comes to machine learning, ADAMS is great for data preprocessing and feature engineering to model training, evaluation, and deployment.
8) JSAT:
JSAT includes popular algorithms such as k-nearest neighbours, support vector machines, decision trees, neural networks, and more. One of the notable features of JSAT is its emphasis on parallel computing and performance optimizations. It leverages multi-core processors and implements parallel algorithms to speed up computations, making it ideal for managing large datasets.
It’s also great in scenarios where data is high-dimensional and contains many zero values - which is something text-based applications, particularly natural language processing will benefit from.
9) JavaML
JavaML emphasizes two things: scalability and efficiency. It uses incremental learning which is particularly useful in scenarios where new data arrives consistently or when resources are limited. In addition to that, it integrates with the distributed computing framework Apache Hadoop, which enables the system to handle large datasets.
What’s Next?
A solid infrastructure is pivotal for organizations to get the most out of machine learning. In 2023, Java is a staple in the machine learning landscape, with ongoing advancements and developments. As we look to the future, integration with emerging technologies, expansion of libraries and frameworks, and collaboration and interoperability will shape the evolution of machine learning in Java.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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