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Lucy Oleschuk
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Java and AI. Why Google, Netflix, and IMB use Java for Machine Learning?

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Artificial Intelligence (AI) is rapidly changing the way we interact with machines and making our lives easier in many ways. However, behind every successful AI application lies a powerful programming language that enables machines to learn and make predictions. Whereas Python is currently the most commonly used programming language for Machine Learning (ML) that enables developers to quickly build apps, Java doesn’t trail behind. It may be the better choice for creating large-scale, high-performance systems thanks to its great performance and scalability. In this article, we will focus on Java’s impact on ML and its benefits for AI apps, as well as tell about tech giants that prefer Java to other programming languages. Java and AI. Why Google, Netflix, and IMB use Java for Machine Learning? - 1

Why Java Is an Ideal Choice for AI Field?

Machine learning has emerged as a crucial component of today's tech landscape, enabling computers to learn from data and improve their performance over time. With the explosion of big data and the increasing demand for automation, machine learning has become an indispensable tool in many industries, including finance, healthcare, e-commerce, and many more. Java plays a vital role in powering AI applications due to its unique features that make it a popular choice for developing ML models. The main benefits that make Java a popular choice for AI include:
  • Ability to solve complex tasks. Java is an object-oriented language, which means it can represent complex real-world entities and processes in a natural and intuitive way.
  • Enhanced security. The language provides advanced security features such as sandboxing, access control, and code signing, making it a secure choice for developing applications that deal with sensitive data.
  • Easy maintainability. Properly-documented Java applications are easier to maintain due to precise programming capabilities.
  • Built-in garbage collection. Since Java can automatically delete useless data, it’s a perfect choice when it comes to developing large-scale projects.
  • Seamless portability. Java is platform-independent, which makes it easy to run the same code on different operating systems and hardware architectures. This feature is especially important for AI applications that require scalability and performance across multiple devices and platforms.
Additionally, Java offers features like easy coding of algorithms, effortless debugging, representation of data in graphics, and good interaction with the user.

Java and Machine Learning

Machine learning is a subfield of AI that enables machines to learn from data and improve their performance over time without being explicitly programmed. It works by using algorithms to identify patterns in data, allowing computers to make predictions and decisions based on that information.
"Machine learning is the next internet." — Tony Tether, former director of DARPA.
Java is a popular programming language used for building ML models due to its flexibility and platform independence. To build an ML model with Java, generally, the steps are as follows: Java and AI. Why Google, Netflix, and IMB use Java for Machine Learning? - 2The first step is to collect and preprocess data. Then, the data is divided into training and testing sets, with the training set used to train the model and the testing set used to evaluate its performance. Once the data is prepared, developers can use Java's machine learning libraries and frameworks to build a model. Nota bene: Java provides a wide range of machine learning libraries and frameworks that offer pre-built algorithms and various functions, helping developers to build and customize their ML models. They also provide features such as data visualization, feature selection, and model evaluation, making it easy to develop and fine-tune ML models. Among the best Java ML libraries and tools to watch out in 2023 and beyond, we can highlight:
  • Deeplearning4j is a Java-based library that provides comprehensive deep learning functionalities, including GPU acceleration, distributed computing, and various neural network architectures. It boasts a GUI-based user interface for hyperparameter tuning.
  • H2O is an open-source machine learning platform designed specifically for big data analytics. It offers automatic machine learning (AutoML) capabilities, grid search, hyperparameter tuning, graphical model selection tools, automated feature engineering tools, and more.
  • Amazon Sagemaker is a managed service offered by Amazon Web Services that simplifies the process of deploying ML applications into production environments. It supports many different frameworks, including TensorFlow, Keras, and custom algorithms written in Java through its SDKs.
  • Matplotlib is an open-source plotting library that is primarily used by data scientists working with Python. Yet, it can be easily adapted to Java programs, enabling users to visualize their results by creating custom plots such as scatter plots or histograms directly from their Java apps.
  • JavaML library provides an API layer between traditional numerical computation libraries and popular ML libraries, enabling users to simplify complex calculations and experiment over large datasets quickly and efficiently.
  • MOA Java ML is a leading ML library for Java developers, letting them use powerful algorithms and tools to create and apply machine learning models. It also includes tools that help developers analyze data, build models, and deploy them to production.
  • Weka is a comprehensive Java ML library that allows users to 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). Additionally, it provides a graphical user interface (GUI) for easy data visualization of datasets and their accompanying results.
  • Stanford CoreNLP is a powerful Java ML library 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 designed for scalability, allowing users to process complex applications easily and develop custom NLP models and applications that work with any other Java library.
  • Accord.NET is one more 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.
  • Apache Mahout offers a scalable ML library that utilizes the MapReduce paradigm and is best for classification, collaborative filtering, and clustering. Mahout uses Apache Hadoop to process multiple parallel tasks and provides recommendation algorithms such as collaborative filtering, facilitating the scalability of building your model quickly.
  • Mallet (Machine Learning for Language Toolkit) is a special tool for natural language processing tasks such as document classification, clustering, topic modeling, and named entity recognition.
As soon as the model is built, it can be deployed using Java's platform-independent features. Java's ability to run on different operating systems and hardware architectures makes it easy to deploy ML models across multiple devices and platforms.

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

It depends on the complexity of the project and the developer's proficiency with the programming language. Simple tasks may take a few weeks to complete, while more intricate projects may take several months. Moreover, using pre-existing libraries and frameworks with pre-built models can substantially speed up the development process. Therefore, it’s quite challenging to estimate the precise timeframe required for constructing one or another ML solution using Java.

How Do AI Apps Benefit from Java?

It's safe to say that Java is revolutionizing the AI landscape by enabling the development of more efficient and scalable ML models. What makes it so special?
  • Java lets developers build complex and customizable algorithms, making it easier to handle large datasets and complex computations.
  • Java's platform independence allows for the deployment of machine learning models across multiple devices and platforms, making it more accessible and scalable.
  • Java's robustness and security features ensure that AI applications are reliable and free from errors.
  • The rich ecosystem of Java's ML libraries and frameworks provides developers with pre-built algorithms and functions that can be easily customized and integrated into their AI applications. This saves them much time and effort, allowing them to focus on building the core features of their AI applications.
Java and AI. Why Google, Netflix, and IMB use Java for Machine Learning? - 3

Top Companies Using Java for ML

As you see, Java is an ideal choice for ML projects. And to further prove this, we’ve come up with the real-life cases of using Java for ML by famous companies.


Though Google has been long developing its own ML frameworks like TensorFlow in Python, the company recently began using Java. That is to say, Google Cloud Platform includes many advanced ML algorithms that can be used through the native Java API. Other Google projects, Image Recognition with Cloud Vision APIs and Natural Language Processing with Cloud Speech-to-Text, are also powered by Java code.


Netflix harnesses the power of ML extensively, relying heavily on Java. Netflix employs a range of ML models to deliver personalized recommendations based on customers' past viewing habits. And to handle the enormous quantities of streaming data in real-time, Netflix's ML framework utilizes a combination of Apache Spark, Kafka Streams, and Java 8. The ML algorithms themselves consist of pure Java code and are deployed onto a cloud-based platform to enable quicker training.


LinkedIn is another well-known company that uses ML algorithms powered by Java mainly. LinkedIn utilizes ML models for suggesting potential employees the best career openings that fit them according to their skill set based on past job experience. LinkedIn's developers mostly use the open-source ML library Apache Mahout, enabling them to quickly implement ML algorithms written in Java code with minimal effort.


Last but not least is the IBM company (we talked about its contribution to cloud computing in the article earlier). Its well-known Watson AI platform uses ML algorithms powered by Java code, letting developers create high-end ML models with greater accuracy than any other existing ML technology available today. Java and AI. Why Google, Netflix, and IMB use Java for Machine Learning? - 4Ultimately, it is clear why many tech giants rely on Java for ML-related tasks. Since the field of AI continues to expand and evolve, Java is emerging as a crucial tool for building and deploying machine learning models. With its versatility, platform independence, and extensive libraries, Java opens the doors for more efficient and scalable ML solutions that are reliable, secure, and easily-integrated with other technologies.

If you're interested in learning more about Java, CodeGym’s self-paced course can immerse you in the basics.

Besides, you can refer to tutorials and books about Machine Learning like:

FAQ about Machine Learning and AI

  • Is machine learning AI? Are machine learning and artificial intelligence the same?

Machine learning is a subset of AI. Artificial intelligence is a broader concept that aims to make computer systems more intelligent and capable of performing tasks that require human intelligence. On the other hand, machine learning focuses on developing algorithms and predictive models that enable those computers to improve their performance on a task through experience. All machine learning is a form of artificial intelligence, but not all artificial intelligence relies on machine learning.
  • Where machine learning is used?

Machine learning is used in various applications across various industries due to its ability to analyze data, identify patterns, and make predictions. Machine learning finds its application in image/speech recognition systems, natural language processing (NLP) systems, recommendation systems, sentiment analysis, predictive maintenance, spam filters in email systems, risk assessment, fraud detection, optimization, and many more.
  • Can I use Java for machine learning?

Yes, of course. While Python may be more popular for creating ML applications, you can also build robust ML applications with Java. Due to its extensive libraries and frameworks for ML, Java is well-suited for developing scalable and enterprise-level ML applications.
  • Is Java good for AI and ML?

Java is suitable for pretty much anything, and AI and ML are no exception. Of course, you can learn AI and ML in various languages, but if you want to implement all those algorithms from scratch to fully understand them, Java is one of the best options. It's a powerful and versatile language with a mature ecosystem and large community. Known for its scalability and performance, Java can help you build various AI and ML applications that can handle substantial amounts of data. And since Java is platform-independent, those applications can run on any platform.
  • Can you train an AI with Java?

Yes, you can train machine learning models, including AI models, using Java. Such Java libraries and tools like the aforementioned Deeplearning4j, Weka, Apache OpenNLP, DL4J, and Encog allow you to perform ML tasks, including training models.
  • What are the 4 types of machine learning?

Machine learning can be generally categorized into four main types:
  • Supervised learning is where the algorithm is trained on a labelled dataset (each input example is associated with a corresponding target or output).
  • Unsupervised learning is where the algorithm is trained on an unlabeled dataset. The algorithm is not provided with explicit output labels but is tasked with finding patterns, relationships, or structures within the data.
  • Semi-supervised learning combines supervised and unsupervised learning, where the algorithm is trained on a dataset containing labelled and unlabeled examples.
  • Reinforcement learning involves an agent learning to make decisions by interacting with an environment. The agent receives feedback through rewards or penalties based on the actions it takes.
Each of these types has specific applications and is suitable for different problems.


As we look to the future of AI, it's clear that Java will continue to play a vital role in the development of ML models and building advanced AI apps. So, whether you're a newbie or a developer looking to expand your skills, Java is an excellent place to start.