The rapidly evolving landscape of Machine Learning (ML) and Large Language Models (LLMs) demands efficient, scalable ways to run distributed workloads for training, fine-tuning, serving, and consuming models. Java, the dominant language in enterprise environments, faces pressure to not only modernize application stacks but also to embrace AI, driven by business needs and the myriad possibilities AI offers.
In this context, LangChain4j has emerged as the leading framework for building GenAI, JVM-powered applications. However, the challenge extends beyond simply calling an LLM from a Java application. How does one build an end-to-end platform from data to a working application? This is where Ray and Kubernetes come into play. Ray, an open-source framework, simplifies distributed machine learning, while Kubernetes streamlines deployment.
This deep-dive session will explore how to combine Java, LangChain4j, Ray, and Kubernetes for ML applications.