Java for Artificial Intelligence and Machine Learning

Many organizations want to use Java’s stability, maturity, and other benefits for training artificial intelligence and machine learning models. Python has become the language of choice for a lot of data scientists and ML engineers. In this post, we examine Java for artificial intelligence. Let’s examine why Java deserves to be the primary choice for training AI and ML models and LLM-based applications in production environments.

In the ongoing artificial intelligence (AI), machine learning (ML), and large language models (LLM) revolution, Python has played a crucial role and has become the standard language for a lot of data scientists and ML engineers. However, as we move from experiments to business implementations, organizations want to rely on Java’s benefits, including its stability and maturity. Let’s review why Java should be the primary choice for training AI and ML models and LLM-based applications in production environments.

Evolution of big data and AI platforms

AI is built on top of data. Lots of data. Many vital infrastructures supporting all that data – such as Apache Spark, Flink, Kafka, and Hadoop – are based on the Java virtual machine (JVM). One of the main reasons is the JVM’s memory management. The evolution of garbage collectors made the JVM the ideal runtime for large-memory applications.

Advantages of Java for AI development

Although Python has a rich ecosystem of libraries for AI development, Java has some distinct advantages for more mature AI systems.

Higher production performance

The Just-In-Time (JIT) compiler in the JVM ensures that the code is always converted to the best-suited native code for the system it’s running on, based on how the code is truly used. This ensures the best performance on any platform the code executes on. Combined with Azul Zing Builds of OpenJDK, this can be further improved with:

• C4 Garbage Collector to remove all garbage collection pauses.
• Falcon compiler for even more improved native code.

Enterprise Integration

Reuse your existing investments in your Java-based systems and development team. Use the tools you already know to develop your AI solution that seamlessly integrates into your current applications and production environments. Utilize your DevOps pipelines, security and monitoring tools, deployment flows, etc.

Higher code quality and maintainability

Java has a track record of delivering complex and scalable solutions while having readable and maintainable code. Java’s static typing helps to catch errors at compile time instead of runtime. New features introduced in recent years, like records and pattern matching, reduce code complexity and make it more readable. Thanks to superior code editors, code refactoring during the development of an evolving codebase has become a no-brainer.

Modern Java AI libraries

The core of Java, the language, and the runtime provide the best environment for developing and running any application. But it’s the rich ecosystem of libraries that enables us to use Java for both training and using AI models.

Deep Netts

Deep Netts‘ tools and algorithms provide intuitive APIs and interfaces for data preprocessing, model training, testing, and integration in production environments. These can be used to integrate computer vision, classification, and regression tasks without the complexity of Python-based frameworks.

• Rapid deployment: seamless integration of ML models into your Java native applications and deployment to production.
• Highly scalable: easy deployment to many devices in heterogeneous environments.
• Accelerated development: developer-friendly, pure Java deep learning library and tools for building and testing machine learning models.
• Easy to use: a machine learning development tool with a step-by-step visual expert guide in an integrated environment for the end-to-end ML development process.
• Cost effectiveness: enables the utilization of existing developer resources instead of hiring additional experts, decreasing employee costs.
• Privacy & Security: secure Java integration because you own and control the data and the models.

Deep Java Library

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It’s designed for Java developers to easily get started, like any other regular Java library, without the need to be a machine (deep) learning learning expert. With your Java experience, you can use machine learning with your favorite IDE in your application. With DJL, building, training, deploying, and integrating your own AI models becomes easy.

Eclipse Deeplearning4J

Eclipse Deeplearning4J (DL4J) is a set of projects for building JVM-based deep learning applications. It helps you to load and preprocess raw data in different formats, and to create a wide variety of deep learning networks. The DL4J stack contains several components, such as DL4J (a high-level API to build MultiLayerNetworks and ComputationGraphs, ND4J (a general-purpose linear algebra library), SameDiff (automatic differentiation and deep learning framework), and more.

LangChain4J

LangChain4j is a Java library that offers unified APIs and a comprehensive toolbox for various LLM providers and embedding stores. It brings you type-safe prompt engineering to build robust Retrieval Augmented Generation (RAG) applications. It also has tools for memory and conversation state management, document loaders and vector stores for knowledge management, and much more.

Spring AI

Spring AI is an application framework for AI engineering, similar to LangChain4J. It fits perfectly in the well-known Spring ecosystem. It abstracts the interfaces to use various LLM- and AI-services with one unified approach by providing POJOs as the building blocks of an application in the AI domain.

AI systems with Java

Experience has shown that Java can bring many benefits to different use cases.

Data processing at scale

While prototyping in Python is convenient, Java leads to better performance for production-grade processing, thanks to:

• More efficient memory utilization thanks to the garbage collectors in the JVM.
• Better parallelism through mature threading models.
• Reduced overhead in high-throughput streaming applications.

Models serving APIs

Java-based API applications to expose models offer many advantages:

• Lower and more predictable latency.
• Better resource utilization.
• More straightforward integration with existing microservice architectures.
• More mature monitoring and observability options.

Foojay webinar

A recent Friends of OpenJDK (Foojay) webinar, Java’s Place in the AI Revolution, discussed these and many other topics, moderated by Azul VP of Developer Relations Pratik Patel. Frank Greco (Senior AI/ML Consultant) and Zoran Sevarac (CEO of Deep Netts) presented the state of AI and Java, and showed a Deep Netts example application to detect bank card fraud.

Conclusion

Do you agree? Would you like to share your experience with AI and Java? I’d love your feedback. Connect with me on LinkedIn and let me know.

Yes, Python helped to quickly develop prototypes to advance AI, ML, and LLMs. However, Java provides a more mature and enterprise-ready platform for production systems, making it a more attractive choice for developing the next generation of applications.

Do you want to see how Azul can boost the performance of your Java applications with our Zing runtime? Contact sales.