Speaker

Tired of treating AI like a black-box REST endpoint? What if you could own the stack: shape the tensors, steer memory, pick execution providers?

In this session, we make that shift. Today, with JDK 25, you can wire real models - LLMs, image classifiers, or object detection algorithms - straight from Java via the Foreign Function and Memory API to call native runtimes like ONNX for fast CPU/GPU inference. You will learn how to map tensor buffers to Java MemorySegment, how to flip execution providers, and have a self-contained Java application. Around the corner, developments in Project Babylon and others will continue to push the boundaries and offer even greater interactions of the JVM with the outside world. We touch on what such a future might mean to Java developers with Project Babylon’s code reflection: express model logic as Java code that Babylon can analyze and lower to accelerator backends, skipping external model files or the need for a glue language.

Build expressive and testable FFM-based inference today and author pure Java AI-ready models with code reflection tomorrow!

Understanding how to monitor and optimize Java applications is fundamental to building efficient software. This hands-on lab will introduce how to capture data to understand your application behavior using JDK Flight Recorder (JFR), manage and analyze JFR recordings, and guide you through creating custom JFR events. We will also cover how JFR can integrate with Prometheus to correlate application and infrastructure behavior, all aimed at enhancing observability. After this lab, you will have the confidence to use JFR to monitor, diagnose, and fine-tune your applications.