Elevate Your Java Skills: ML & AI for Java Development

Unlock the potential of AI and transform vast data into actionable insights. Harness the power of cutting-edge AI tools like Copilot and ChatGPT, gain insights into the workings of Large Language Models (LLMs), and learn effective strategies for integrating advanced AI features into your projects.

GenAI & Data Engineering

Learn from Industry Leaders about:

  • Big Data Landscape: Explore the evolution of data storage technologies and advanced tools for efficiently processing unstructured data.
  • AI-Powered Software Development: Leverage tools like Copilot, ChatGPT, or Gemini to enhance code writing, streamline debugging, and boost overall software development efficiency.
  • Demystifying LLMs: Gain a comprehensive understanding of Large Language Models (LLMs). Explore their operational mechanics and capabilities across diverse applications.
  • Machine Learning with Java: Explore robust Java frameworks and libraries tailored for developing intelligent applications powered by machine learning.
  • AI Feature Development: Integrate AI services and functionalities effectively into your projects to enhance capabilities and user experiences.
  • Building Chatbots with Cloud Services: Master the essentials of creating practical chatbots using readily accessible cloud-based tools and services.

Track Speakers 2025

Track Program 2025

Track Sessions on Jax London

Track Sessions on Jax London

View all sessions

Frequently Asked Questions

What is the aim of the "GenAI & Data Engineering" track at JAX London?

This track empowers Java developers to harness AI and transform data into actionable insights. It covers advanced tools like Copilot and ChatGPT, builds understanding of LLMs (Large Language Models), and teaches how to integrate AI features seamlessly into development workflows.

Which key topics are covered by this track?

Attendees can expect to explore: The evolution of Big Data and tools for processing unstructured information. AI-powered software development, using tools like Copilot, ChatGPT, or Gemini. A deep dive into LLMs, uncovering their structure and use cases. Machine learning with Java, using Java-specific frameworks. AI feature development in applications. Building chatbots using cloud services.

Is there content that focuses on making ML techniques more aligned with software development practices?

Yes. A dedicated session—"Agile Machine Learning: From Theory to Production"—explores how to apply software engineering best practices to ML, integrating research into agile development cycles.

What data engineering topics are featured?

Several practical topics include: An introduction to data science fundamentals, helping participants understand prediction methodologies. Reactive database access patterns, which promote resilient and efficient data handling in systems. Real-world use cases involving Kafka and Kafka Connect, addressing enterprise data integration challenges.

Are there innovative intersections of AI and data technologies included?

Absolutely. For instance, the session "Pairing Machine Learning + Blockchain for Enhanced Data Tracking, Insights, and Security" demonstrates how combining machine learning with blockchain technologies can improve transparency, security, and traceability in data workflows.

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