Kevin Dubois
Principal Developer Advocate at Red Hat
Brussels, Belgium
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Kevin is a Java Champion, software engineer, author and international speaker with a passion for Open Source, Java, and Cloud Native Development & Deployment practices. He currently works as developer advocate at Red Hat where he gets to enjoy working with Open Source projects and improving the developer experience. He previously worked as a (lead) software engineer at a variety of organizations across the world ranging from small startups to large enterprises and even government agencies.
Kevin is actively involved in Open Source communities, contributing to projects such as Quarkus, Knative, Apache Camel, and Podman (Desktop). He is also a member of the Belgian CNCF and the Belgian Java User Group.
Kevin speaks English, Dutch, French and Italian fluently and is currently based in Belgium, having lived in Italy and the USA as well.
In his free time you can find Kevin somewhere in the wild hiking, gravel biking, snowboarding or packrafting.
Awards
Welcome to the AI Jungle! Now What?
(Generative) AI has taken the world by storm and acted as an accelerant to business transformation. Sure, it can potentially increase efficiency and productivity, but doesn’t it feel a little bit like the Wild, Wild West out there? Many “regular” application developers are overwhelmed with having to deal with yet another paradigm shift and a new set of things to learn. The sprawl of IT tools in organizations gets bigger, now also grappling with finding, building, serving and using AI models. Even Data Scientists, creating models themselves, are often not sure where to go once they’ve experimented, trained and refined models.
Fortunately, the open source world offers a wonderful opportunity to standardize and democratize the way we work with AI. Thanks to exciting new open source projects, we can now create, use and serve AI models, as well as build platforms that can provide an optimal developer experience for both data scientists and software developers. This allows us to build and deliver AI models and AI-infused applications in a repeatable, secure and enterprise way using tried-and-true technologies and methodologies such as containers, Kubernetes, CI/CD and GitOps.
Join this session as we share a series of live demos with Open Source innovations available today, featuring projects such as OpenDataHub (OpenShift AI), Kubernetes, and Backstage to accelerate development cycles and optimize your releasing performance. We will also integrate Open Source models into Java applications, even in a fully local development environment.
Serverless Java in Action: Cloud Agnostic Design Patterns and Tips
You've probably seen how to create a Function-as-a-Service with one of the cloud providers, but if this is all you know about Serverless, prepare to have your mind blown!
In this session we'll show you how to create a production-grade, cloud-agnostic, event-driven serverless solution with Quarkus, a Java stack optimized for fast startup and small footprint; and Knative, an open source community project for deploying, running and managing serverless applications on Kubernetes. Say goodbye to vendor lock-in and hello to Supersonic Subatomic Java-based Serverless bliss!
Create AI-Infused Java Apps with LangChain4j
Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” Java application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?
Fortunately, we can actually infuse AI models built by actual AI experts into our applications fairly straightforwardly, thanks to some new projects out there. We promise it’s not as complicated as you might think! Thanks to the ease of use and superb developer experience of Quarkus and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy :)
In this lab, you’ll explore a variety of AI capabilities. We’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll get our hands dirty with writing some code and exploring LangChain4j features such as prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); and discovering ways to run (and train) models locally using tools like Ollama and/or Podman AI Lab. In addition, you’ll add observability and fault tolerance to the AI integration and compile the app to a native binary. You might even try new features, such as generating images or audio!
Come to this session to learn how to build AI-infused applications with Java and Quarkus.
Note: We can provide a virtual environment for you but if you want to run it on your own laptop, you should have Java 17+ installed (better if it's Java 21), and a container runtime such as Podman or Docker. If you need help with the setup, visit this page: https://redhat-developer-demos.github.io/quarkus-tutorial/quarkus-tutorial/01_setup.html
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