Top 10 Java Machine Learning Libraries & Tools: Famous Firms Which Use Java Machine Learning

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 knowing Java Machine Learning Libraries 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.

 

Importance of ML in Java

Machine Learning (ML) is a field of artificial intelligence that involves creating algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed for every task. While Java is not the most common language used for ML compared to languages like Python or R, there are still several important reasons why Machine Learning in Java holds significance:

 

• Ecosystem and Libraries: Java has a rich ecosystem of libraries and frameworks that can be utilized for machine learning tasks. For example, libraries like Deeplearning4j, Weka, and MOA provide tools for various aspects of ML, such as deep learning, data preprocessing, and more.
• Integration with Existing Java Codebase: Many organizations have existing applications and systems written in Java. Using Java for ML allows these organizations to seamlessly integrate machine learning functionality into their existing codebase, without requiring a complete language switch.
• Performance and Scalability: Java is known for its performance and scalability. It can handle large-scale machine learning tasks efficiently, making it suitable for projects that involve processing extensive datasets and require robust performance.
• Enterprise Applications: Java is widely used in enterprise environments. Machine learning can be applied to solve complex business problems such as fraud detection, customer segmentation, and demand forecasting. Java's familiarity in such environments can make it easier to adopt and implement ML solutions.
• Security: Java is known for its security features, which can be crucial when dealing with sensitive data in machine learning applications. The security features of Java can help ensure that ML models and data are protected from unauthorized access and tampering.
• Cross-Platform Compatibility: Java's "write once, run anywhere" philosophy allows ML applications developed in Java to be deployed across various platforms without major modifications. This can simplify the deployment process and reduce compatibility issues.
• Community Support: Although the ML community in Java might not be as large as in Python, it is still active. There are forums, online communities, and resources available to help developers solve problems and share knowledge related to Java-based machine learning.
• Educational Purposes: Java is often used as an introductory programming language in computer science education. Teaching machine learning concepts using Java can provide students with a strong foundation in both programming and ML principles.
• Diverse Use Cases: Java can be applied to various ML use cases, such as natural language processing, image recognition, anomaly detection, recommendation systems, and more. Its versatility allows developers to explore a wide range of ML applications.

 

In summary, while Java might not be the first choice for many machine learning practitioners due to the popularity of languages like Python, it still offers significant advantages in terms of integration, performance, security, and compatibility in specific contexts, especially when working within the Java ecosystem or existing applications.

 

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.

 

Key Features:

 

• Model Serving: TensorFlow Serving is specifically designed for serving machine learning models in production environments, ensuring optimal performance and scalability.
• Efficient Loading: It supports dynamic model loading, allowing you to update models without interrupting the serving process.
• Versioning: TensorFlow Serving enables you to manage different versions of models, making it easy to roll back or switch to new models.
• RESTful API: It provides a RESTful API for easy integration with various applications and systems.
• Monitoring and Metrics: TensorFlow Serving offers built-in metrics and monitoring capabilities, helping you keep track of model performance and health.

 

Pros:

 

• Scalability: TensorFlow Serving is designed to handle high loads and can be deployed in distributed environments, ensuring smooth scaling.
• Efficiency: It optimizes model inference, making predictions faster and reducing latency.
• Flexibility: It supports a variety of model formats, including TensorFlow models and others, offering flexibility in deployment.
• Version Management: The ability to serve multiple model versions simultaneously simplifies A/B testing and gradual model rollouts.
• Integration: TensorFlow Serving can be easily integrated into existing applications and microservices architecture.

 

Cons:

 

• Complex Setup: Setting up and configuring TensorFlow Serving can be more complex compared to simple model deployment methods.
• Resource Requirements: Running TensorFlow Serving may require significant computational resources, especially for high-demand applications.
• Limited to TensorFlow: While it excels at serving TensorFlow models, it might not be the best choice for serving models developed with other frameworks.

 

Use Cases:

 

• Online Predictions: TensorFlow Serving is ideal for providing real-time predictions in applications like recommendation systems, fraud detection, and personalized content delivery.
• Image and Speech Recognition: It can serve deep learning models for tasks such as image classification, object detection, and speech recognition.
• Natural Language Processing: TensorFlow Serving is suitable for deploying models that process and generate natural language, like chatbots and sentiment analysis.
• Healthcare and Finance: In industries like healthcare and finance, TensorFlow Serving can be used for predictive modeling and risk assessment.

 

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. 

 

Key Features:

 

• Scalability: Apache Spark MLlib can handle large-scale data and perform distributed machine learning computations.
• Rich Algorithms: It offers a wide range of machine learning algorithms for classification, regression, clustering, and more.
• Integration: MLlib seamlessly integrates with other Spark components, making it suitable for big data pipelines.
• Streaming Support: It includes tools for real-time data processing and streaming analytics.
• Ease of Use: MLlib provides high-level APIs in Java, Scala, Python, and R, simplifying development.

 

Pros:

 

• Distributed Processing: Apache Spark MLlib enables distributed processing, accelerating model training and evaluation.
• Versatility: It supports both batch and real-time data processing, accommodating various use cases.
• Integration with Spark: MLlib is part of the broader Apache Spark ecosystem, allowing integration with Spark's other components.
• Scalability: It can scale horizontally to handle massive datasets and complex computations.
• Community and Support: Apache Spark has a vibrant community and extensive documentation.

 

Cons:

 

• Learning Curve: Working with distributed systems like Spark may have a steeper learning curve for beginners.
• Resource Requirements: Processing large datasets requires substantial computational resources.
• Limited to Spark: MLlib's integration with Spark means it may not be as versatile when used outside the Spark ecosystem.
• Algorithm Coverage: While MLlib provides a range of algorithms, it might not offer the same depth as specialized libraries.

 

Use Cases:

 

• Large-Scale Data Analysis: MLlib is ideal for analyzing and extracting insights from massive datasets.
• Predictive Modeling: It's used for building predictive models in applications like recommendation systems and fraud detection.
• Clustering and Segmentation: MLlib's clustering algorithms find use in customer segmentation and market analysis.
• Natural Language Processing: It's employed for text classification, sentiment analysis, and language modeling.
• Anomaly Detection: MLlib can detect anomalies in diverse domains like cybersecurity and industrial monitoring.

 

 

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. 

 

Key Features:

 

• Deep Learning Framework: Deeplearning4j is a comprehensive deep learning framework for Java and JVM languages.
• Multi-Layer Networks: It supports building complex neural networks with multiple layers and architectures.
• Parallel Processing: Deeplearning4j leverages parallel processing to accelerate training on multi-core CPUs and GPUs.
• Distributed Training: It enables distributed training across clusters, improving efficiency for large-scale models.
• Integration: Deeplearning4j can be integrated with Hadoop and Spark for big data processing.

 

Pros:

 

Java Compatibility: Being Java-based, it seamlessly integrates with existing Java applications and ecosystems.

Performance: It's optimized for efficient processing on CPUs and GPUs, delivering fast model training.

Scalability: Deeplearning4j supports distributed training and scales well for large datasets.

Multi-Platform: It works on various platforms and supports Android and IoT deployments.

Variety of Architectures: Supports various neural network architectures, including convolutional and recurrent networks.

 

Cons:

 

Learning Curve: Deep learning concepts can be complex, requiring users to have a solid understanding of neural networks.

Community Size: While active, the community might be smaller compared to larger deep learning frameworks.

Java-Centric: If you're not already familiar with Java, the learning curve might be steeper.

Advanced Features: Some advanced features available in other frameworks might be more limited in Deeplearning4j.

 

Use Cases:

 

Image Recognition: Deeplearning4j is used for image classification, object detection, and facial recognition.

Natural Language Processing: It's employed in tasks like sentiment analysis, language translation, and chatbots.

Anomaly Detection: Used for identifying anomalies in data, such as fraud detection in finance.

Predictive Analytics: Deeplearning4j is applied to predictive modeling in various industries, from healthcare to marketing.

Recommendation Systems: It's used to build recommendation engines in e-commerce and content platforms.

 

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!  

 

Key Features:

 

• Scalability: H2O supports distributed computing, allowing scalable and parallel processing for large datasets.
• Algorithms: It offers a wide range of machine learning algorithms for classification, regression, clustering, and more.
• AutoML: H2O AutoML automates the model selection and hyperparameter tuning process.
• Integration: H2O integrates with popular programming languages like Python and R, and can be used with Hadoop and Spark.
• Interpretability: It provides tools to interpret and explain model predictions.

 

Pros:

 

Ease of Use: H2O's intuitive interface and AutoML feature simplify the model development process.

Performance: It leverages distributed computing to deliver fast model training and evaluation.

 

Cons:

 

Resource Intensive: Large-scale distributed training might require substantial computational resources.

Limited Customization: While AutoML is convenient, experienced users might want more control over model tuning.

 

Use Cases:

 

Predictive Modeling: H2O is used for building predictive models in scenarios like finance and marketing.

Anomaly Detection: It's applied to detect anomalies and outliers in various domains.

Healthcare Analytics: Used for predicting patient outcomes and disease diagnosis.

Customer Segmentation: H2O assists in customer profiling and segmentation for targeted marketing.

Credit Scoring: Employed for credit risk assessment and fraud detection.

 

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.

 

Key Features:

 

• Managed Service: Amazon SageMaker is a fully managed machine learning service that handles the end-to-end machine learning lifecycle.
• Built-in Algorithms: It provides a variety of pre-built algorithms for common machine learning tasks.
• AutoML: SageMaker AutoML automates model selection, hyperparameter tuning, and deployment.
• Scalability: It scales easily to handle large datasets and complex models.
• Integration: SageMaker integrates seamlessly with other AWS services and tools.

 

Pros:

 

Scalability: It scales effortlessly to handle growing data volumes and demand.

Integration: Tight integration with AWS services streamlines data processing and model deployment.

One-Stop Solution: Provides tools for data preprocessing, model training, and deployment in one platform.

 

Cons:

 

Cost: While convenient, the usage costs can increase as your machine learning operations scale.

Customization: Advanced users might find certain aspects of SageMaker less customizable compared to other platforms.

 

Use Cases:

 

Image and Video Analysis: Used for tasks like image classification, object detection, and video analysis.

Natural Language Processing: SageMaker applies to sentiment analysis, language translation, and chatbots.

Predictive Analytics: Employed for predicting customer behavior, sales trends, and more.

Recommendation Systems: Used to build personalized recommendation engines in e-commerce and content platforms.

Healthcare and Finance: Applied in healthcare for diagnosing diseases and in finance for fraud detection.

 

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! 

 

Key Features:

 

• Data Visualization Library: Matplotlib is a widely used Python library for creating static, interactive, and animated visualizations.
• Customization: Offers extensive customization options for plot appearance and styles.
• Multiple Plot Types: Supports various plot types including line, bar, scatter, histogram, and more.
• Publication-Ready Output: Produces high-quality, publication-ready plots for reports and presentations.
• Integration: Works well with popular data analysis libraries like NumPy and pandas.

 

Pros:

 

• Versatility: Matplotlib covers a wide range of visualization needs, from basic to complex.
• Open Source: Being open source encourages community contributions and supports its widespread use.
• Compatibility: Matplotlib can be used across platforms and integrated into different Python environments.
• Educational Tool: Often used for teaching data visualization due to its simplicity.

 

Cons:

 

• Learning Curve: Some customization and complex plots might require familiarity with Matplotlib's syntax.
• Boilerplate Code: For complex visualizations, the code required can become lengthy.
• Interactivity: While it supports interactivity, it might not offer the same level as more specialized libraries.

 

Use Cases:

 

• Data Exploration: Matplotlib aids in exploring and visualizing data distributions and patterns.
• Data Communication: Used to create informative visualizations for presentations and reports.
• Statistical Analysis: Matplotlib assists in depicting statistical results through various plots.
• Scientific Visualization: Applied in creating plots for scientific research and data analysis.
• Educational Purposes: Often used for teaching data visualization concepts in data science courses.

 

 

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!

 

Key Features:

 

• Java Machine Learning Library: JavaML is a machine learning library specifically for Java programming.
• Algorithm Variety: Offers a range of machine learning algorithms for diverse tasks.
• Integration: Seamlessly integrates into Java applications for easy use.

 

Pros:

 

• Java Integration: Works well within Java ecosystems and applications.
• Algorithmic Diversity: Provides various algorithms suitable for different machine learning scenarios.
• Ease of Integration: Its compatibility with Java simplifies integration and development.

 

Cons:

 

• Limited Resources: JavaML's user base and resources might be more limited compared to larger libraries.
• Advanced Features: Might have less extensive advanced features compared to more mature libraries.

 

Use Cases:

 

• Java Applications: JavaML is ideal for incorporating machine learning into existing Java projects.
• Custom Solutions: Used to develop custom machine learning solutions tailored to specific needs.
• Research and Experimentation: Applied for research and experimenting with machine learning algorithms.

 

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.

 

Key Features:

 

• Massive Online Analysis: MOA is designed for stream processing and online machine learning.
• Algorithms: It offers a wide range of algorithms for data stream analysis and incremental learning.
• Real-Time: MOA enables real-time analysis of streaming data.
• Extensibility: Users can easily add their own algorithms and evaluate them within the MOA framework.

 

Pros:

 

• Stream Processing: MOA excels in handling continuous streams of data for real-time analysis.
• Incremental Learning: It's suitable for scenarios where data arrives in chunks and models need to be updated.
• Algorithmic Variety: Offers a variety of algorithms tailored for streaming data analysis.
• Research-Focused: Often used in research and experimentation in online machine learning.

 

Cons:

 

• Specialized Use: MOA is primarily designed for online machine learning, making it less suitable for batch processing.

 

Use Cases:

 

• IoT Data: MOA can process streaming data from sensors and IoT devices.
• Financial Data: Applied in real-time analysis of financial market data.
• Social Media Monitoring: Used for analyzing streaming social media data.
• Network Intrusion Detection: MOA can be used to detect anomalies in network traffic in real-time.
• Dynamic Environments: Suitable for situations where data distribution changes over time.

 

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). 

 

Key Features:

 

• Machine Learning Toolkit: Weka is a widely used open-source machine learning toolkit.
• Graphical Interface: It provides a user-friendly graphical interface for data preprocessing, modeling, and evaluation.
• Algorithms: Offers a diverse set of machine learning algorithms for classification, regression, clustering, and more.
• Data Visualization: Weka includes tools for visualizing data and model performance.

 

Pros:

 

• Ease of Use: Its graphical interface simplifies data analysis, modeling, and experimentation.
• Algorithms: Provides a wide variety of algorithms suitable for different machine learning tasks.
• Educational Tool: Often used for teaching machine learning concepts due to its user-friendly interface.
• Community Support: Benefits from an active user community and online resources.

 

Cons:

 

• Limited Scalability: Weka might not be the best choice for handling extremely large datasets or distributed computing.
• Graphical Interface Dependency: Advanced users might find the graphical interface limiting for complex tasks.
• Learning Curve: While user-friendly, some machine learning concepts still require understanding.

 

Use Cases:

 

• Educational Purposes: Often used to teach machine learning concepts in academic settings.
• Exploratory Data Analysis: Weka helps analysts explore and understand datasets.
• Model Prototyping: It's used to quickly prototype machine learning models and evaluate their performance.
• Pattern Recognition: Applied in tasks like image classification, text categorization, and more.
• Data Mining: Weka is employed for discovering insights and patterns in large datasets.

 

 

Stanford CoreNLP

Stanford CoreNLP Java ML will be an invaluable tool for developers and machine learning professionals in 2023 and beyond. 

 

Key Features:

 

• NLP Toolkit: Stanford CoreNLP is a comprehensive natural language processing toolkit.
• Multilingual Support: It supports multiple languages for various NLP tasks.
• Various Annotations: Provides annotations like part-of-speech tagging, named entity recognition, sentiment analysis, and more.
• Integration: Can be easily integrated into Java applications.

 

Pros:

 

• Robust NLP: Offers a range of NLP capabilities, including parsing, tokenization, and coreference resolution.
• Multilingual: Supports a wide range of languages, making it suitable for global applications.
• Easy Integration: Integrates seamlessly with Java applications for streamlined NLP processing.
• Research-Backed: Developed by Stanford researchers, CoreNLP leverages cutting-edge research.

 

Cons:

 

• Java Dependency: Being Java-based might limit its integration with non-Java platforms.
• Resource Intensive: Some tasks can be computationally intensive and require significant resources.
• Learning Curve: Understanding and using advanced NLP features may require familiarity with NLP concepts.

 

Use Cases:

 

• Text Analysis: Used for tasks like sentiment analysis, named entity recognition, and coreference resolution.
• Information Extraction: Extracts structured information from unstructured text, such as extracting relationships between entities.
• Language Understanding: CoreNLP aids in parsing sentences and understanding grammatical structure.
• Question Answering: Applied in building systems that answer questions based on textual information.
• Chatbots: Used to power conversational agents by analyzing and generating human-like text.

 

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.

 

Key Features:

 

• .NET Framework: Accord.NET is a machine learning framework specifically designed for the .NET platform.
• Diverse Algorithms: It offers a comprehensive set of algorithms for image and signal processing, statistical analysis, and machine learning.
• Modularity: Accord.NET's modular structure allows users to select and use only the components they need.
• Open Source: Accord.NET is an open-source library, enabling customization and contribution.

 

Pros:

 

• Integration: Seamlessly integrates with existing .NET applications and ecosystems.
• Diverse Algorithms: Provides a wide range of algorithms for various machine learning and data analysis tasks.
• Flexibility: Modular design lets users select specific components relevant to their projects.

 

Cons:

 

• Scope: While comprehensive, it might not cover the same breadth and depth as larger frameworks.
• Resource Usage: Depending on the complexity of the algorithms, computational resources might be required.

 

Use Cases:

 

• Image Processing: Used for tasks like image classification, object detection, and feature extraction.
• Signal Processing: Applied in speech recognition, audio analysis, and sensor data interpretation.
• Data Analysis: Accord.NET assists in statistical analysis, data mining, and pattern recognition.
• Machine Learning: Used for building predictive models in various domains, such as finance and healthcare.
• Computer Vision: Employed for facial recognition, object tracking, and video analysis.

 

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.

 

Read also: How to Build an App with ChatGPT

 

 

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.

 

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.