Deliver AI infused app innovation with Open Liberty on AKS

Introduction

Introduction

In this article, we'll explore how to deliver AI-infused application innovation using Open Liberty on Azure Kubernetes Service (AKS). With a focus on ease of use and rapid deployment, this guide is ideal for developers who want to integrate AI capabilities into their applications without extensive prior experience in AI.

Don't Panic: Embracing Innovation

As we delve into AI, it’s essential to remember that there's no need to panic. Many experts have witnessed trends come and go, but what's crucial to realize is that each wave of innovation, including AI, comes with significant incentives from vendors to facilitate adoption. The push for adoption is backed by considerable financial investment from AI vendors who aim to make the technology easily accessible and productive for developers.

Learn from the Past

James Gosling, one of the founders of Java, once shared insights on easing the adoption of new technologies. By presenting new concepts through familiar formats, such as using curly braces in Java, developers found it easier to transition from older technologies. This approach is mirrored in the AI ecosystem, where existing Java patterns—like the Builder pattern and CompletableFuture for asynchronous programming—are being utilized to integrate AI capabilities in a familiar manner.

Major Parts of the Demo

This demo centers around IBM Open Liberty on AKS, showcasing a cross-vendor ecosystem. Microsoft aims to support Java applications regardless of the runtime stack you choose.

Getting Started with Open Liberty

Open Liberty is a lightweight, modular runtime stack that supports cloud-native, microservices-ready architectures. It implements two significant Java specifications: Jakarta EE and MicroProfile. The demo, prepared by Gilbert Quan and Grace Jansen from IBM, employs dependency injection with CDI, MicroProfile Config, and MicroProfile Metrics.

Deployment Steps

Deploying the demo involves several straightforward steps that include:

1. Running a Maven clean install.
2. Building a Docker image.
3. Pushing the image to a container registry.
4. Starting the offer and deploying the application to AKS.

Using the Azure portal, you would search for "Open Liberty on Kubernetes," create a new Resource Group, specify an existing Azure container registry (where the image is pre-pushed), select a load balancer, and ultimately deploy the application.

Demo Walkthrough

Upon creating the deployment, the portal provides useful outputs detailing how to access the application, connect to the Kubernetes cluster, and view deployment specifics, including YAML files and necessary commands.

The application itself is a chat interface, utilizing two models for AI chat: Azure OpenAI and Hugging Face. This dual system ensures redundancy if one service is unavailable.

MicroProfile Metrics allows for collecting and exposing metrics with minimal effort. The application is designed to respond to typical queries about MicroProfile and Jakarta EE, showcasing the integration between AI and the Java ecosystem.

Conclusion

This article has illustrated how to easily integrate AI into Jakarta EE applications using Open Liberty on Azure. With comprehensive support from Microsoft and IBM, the steps to deploy and manage AI-infused applications have never been more accessible.

Keywords

• AI
• Open Liberty
• Azure Kubernetes Service (AKS)
• Jakarta EE
• MicroProfile
• Deployment
• Docker
• Chatbot
• MicroProfile Metrics
• Containerization

FAQ

Q: What is Open Liberty?
A: Open Liberty is a lightweight, modular runtime stack designed for cloud-native applications, supporting microservices architectures and major Java specifications.

Q: How do I get started with AI integration in Open Liberty?
A: You can start by following the deployment steps outlined in the article. These include setting up your project in Azure, configuring Docker, and utilizing MicroProfile features for ease of integration.

Q: Can I use AI models from different providers?
A: Yes, the demo illustrates the capability of using multiple AI models, such as Azure OpenAI and Hugging Face, to ensure reliability through redundancy.

Q: What tools do I need for deployment?
A: You’ll primarily need Maven for building your project, Docker for containerization, and access to Azure for deploying the application on AKS.

Q: How can I monitor my AI application?
A: You can use MicroProfile Metrics, which allows for easy collection and exposure of application metrics.