Java Language & PlatformHands-on Lab120min
Java is for Data Science, Too: Building an End-to-End ML Pipeline Without Leaving the JVM
This practical session shows how Java can handle end-to-end data science without switching to Python. Using JTaccuino, Hardwood, dflib, DeepNetts, and a new plotting API, participants will build a complete predictive machine learning pipeline in Java, from data exploration to visualization and neural network training.
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Sven ReimersAirbus
Zoran SevaracDeep Netts
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Monday, October 5, 10:30-12:30
TBA 7
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For years, Python has reigned supreme in the data science and machine learning landscapes, leaving Java developers with a lingering sense of FOMO. But things have changed. With the advent of lightweight data frames, blazing-fast native file parsers, and interactive notebooks, Java has evolved into a formidable environment for data engineering, exploration, and deep learning.
In this code-driven, interactive and hands-on session, we will break down the myth that you have to context-switch to Python for data science. We will build a complete predictive machine learning pipeline entirely in Java, utilizing an elite stack of modern JVM tools:
JTaccuino: For interactive, Jupyter-style exploratory data analysis directly inside a Java notebook environment.
Hardwood: The ultra-fast, zero-dependency Apache Parquet reader optimized for modern multi-core JVM performance to ingest large datasets.
DFLib:A lightweight, expressive Java DataFrame library to clean, filter, and manipulate our data.
DeepNetts: A pure Java based deep learning development toolkit for building, training, and evaluating neural networks, featuring CPU or GPU acceleration.
gog4j: An exclusive first look at a highly anticipated, unreleased Java plotting API based on the grammar of graphics to natively visualize data and model metrics right in the notebook.
Paging all Java Developers interested in doing math, science, AI, QC and more with Java - join us to shape Java's future in scientific computing.
In this code-driven, interactive and hands-on session, we will break down the myth that you have to context-switch to Python for data science. We will build a complete predictive machine learning pipeline entirely in Java, utilizing an elite stack of modern JVM tools:
JTaccuino: For interactive, Jupyter-style exploratory data analysis directly inside a Java notebook environment.
Hardwood: The ultra-fast, zero-dependency Apache Parquet reader optimized for modern multi-core JVM performance to ingest large datasets.
DFLib:A lightweight, expressive Java DataFrame library to clean, filter, and manipulate our data.
DeepNetts: A pure Java based deep learning development toolkit for building, training, and evaluating neural networks, featuring CPU or GPU acceleration.
gog4j: An exclusive first look at a highly anticipated, unreleased Java plotting API based on the grammar of graphics to natively visualize data and model metrics right in the notebook.
Paging all Java Developers interested in doing math, science, AI, QC and more with Java - join us to shape Java's future in scientific computing.
Zoran Sevarac
Zoran Sevarac is a seasoned software engineer, university professor of Artificial Intelligence, and passionate Java developer with more than two decades of experience in software engineering, AI research, and open-source innovation.
He is the creator of two pioneering AI development platforms built entirely in Java — Neuroph and Deep Netts — which made AI development more accessible to the Java community. His work bridges modern AI technologies with the reliability, performance, and scalability of the Java ecosystem.
A Duke’s Choice Award winner, Java Champion, and member of the JCP Executive Committee, Zoran has been instrumental in driving the evolution of AI support in Java. His mission is to simplify AI adoption, enable GPU acceleration through Project Panama and the Vector API, and bring high-performance, energy-efficient, and FinOps-friendly AI to enterprise systems.
He is the creator of two pioneering AI development platforms built entirely in Java — Neuroph and Deep Netts — which made AI development more accessible to the Java community. His work bridges modern AI technologies with the reliability, performance, and scalability of the Java ecosystem.
A Duke’s Choice Award winner, Java Champion, and member of the JCP Executive Committee, Zoran has been instrumental in driving the evolution of AI support in Java. His mission is to simplify AI adoption, enable GPU acceleration through Project Panama and the Vector API, and bring high-performance, energy-efficient, and FinOps-friendly AI to enterprise systems.