Kevin Dubois
Senior 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.
Tackling Inner Loop Development for Distributed Systems
You’ve all been there, haven’t you? You are developing an application that needs to connect to something else in order to function properly, yet you can’t (or don’t want to) run that “something else” on your local developer laptop. What are some of the tools and technologies that could help in this situation?
Microservices and distributed systems in general are often deployed on a cloud platform such as Kubernetes, which orchestrates the deployment and management of containerized applications. Microservices, however, don't exist in a vacuum. They typically communicate with other services, such as databases, message brokers, or other microservices. This distributedness makes it hard for you to do local/inner-loop development and testing efficiently.
In this talk, you will learn about some potential solutions you can use to help tackle some of these challenges by either running services locally in a more efficient way or by interacting with external dependencies/systems remotely.
You should come away from this session with some new ideas on how to work with distributed applications using technologies such as Docker Compose, TestContainers, Quarkus Dev Services, Remote Development, Skupper, and Eclipse JKube.
Progressive Delivery and Experimentation on Production with Argo Rollouts
In this hands-on workshop attendees will learn the basics of how to use Argo Rollouts to progressively roll out apps to production while automatically monitoring and rolling back if issues arise based on the metrics we’re collecting. In this lab you will learn how to:
* Review the Rollouts specification
* Migrate an existing Deployment resource to a Rollout kind
* Use Rollouts to perform a blue-green deployment
* Enhance the blue-green rollout with integrated testing by applying an AnalysisTemplate
* Use Rollouts to perform a canary deployment
* Observe the canary progressive delivery in action
* Observe the remainder of the deployment until completion
* Perform a rollback of the canary of the canary deployment
* Learn how to apply the new Experiment CRD of Argo Rollouts to experiment with a subset of your deployment
Next-gen CI/CD with Gitops and Progressive Delivery
Even though you might be using Continuous Integration and Delivery, chances are you are still not 100% sure things will roll out without a glitch once you go to production. There will always be differences between environments and a risk for unforeseen issues related to your production environment and/or actual load, leading to potential disruption to your customers.
Progressive delivery is the next step after Continuous Delivery to test your application in production before it becomes fully available to all your user bases.
Embrace GitOps and Progressive Delivery with techniques like blue-green, canary release, shadowing traffic, dark launches and automatic metrics-based rollouts to validate the application in production using Kubernetes and tools like Istio, Prometheus, ArgoCD, and Argo Rollouts.
Come to this session to learn about Progressive Delivery in action using Kubernetes. And to have some fun as well as we release a car racing game you can participate in :)
Kubernetes. From 0 to Production-Grade with Java
I hear you thinking, “is this yet another session about doing cloud native development?”. Well.. yes, sort of, but we promise it’s going to be a good one - or so we think :).
In this session we’re going to talk (and demo!) about what it means to be a good Cloud Citizen - which is more than merely putting an application in a container and deploying it. To optimize your (Java) application's performance and resilience in the cloud, it is essential to consider factors such as providing real-time health status through fine-grained metrics. You’ll also need to ensure fast startup, and avoid excessive resource consumption within the cluster.
Being a good Cloud Citizen also involves streamlining configuration, deployment and upgrade processes. By integrating these tasks seamlessly, the application can facilitate smooth deployments and upgrades, which will lead to more efficiency and ease of management.
Join us in this session and embark on a journey to develop a perfect(ish) Cloud Native Java Application with Quarkus. This journey will encompass a comprehensive understanding of both the development and operational perspectives, aiming to optimize applications for the cloud environment. You will get an overview of the Kubernetes basics from a Java developer’s perspective and learn step-by-step how to get your application production ready on Kubernetes.
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.
Jfokus 2025 Sessionize Event Upcoming
Tampa JUG Upcoming
Welcome to the AI jungle, Now What?
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Is Serverless Powerfully Powerless?
JavaCro'24 Sessionize Event
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Welcome to the AI Jungle! Now What?
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Taming Kubernetes: Streamlining Inner-loop Development for Distributed Systems
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Taming Kubernetes: Streamlining Inner-loop Development for Distributed Systems
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Distributed Tracing Integration with Opentelemetry and Knative
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Javazone
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Progressive Delivery in the Kubernetes Era
Voxxed Days Luxembourg
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JCON EUROPE 2023 Sessionize Event
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JNation 2023 Sessionize Event
KCD Colombia
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Code Europe
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Red Hat Tech Exchange
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