Introduction In this blog post, we’ll explore how Apache Camel JBang’s Infrastructure Command can help you rapidly prototype end-to-end integration scenarios and adapt to changing requirements. We’ll walk through a realistic development scenario where requirements evolve over time, demonstrating how Camel’s flexibility makes it an ideal choice for proof-of-concept development. Camel JBang Infrastructure: Your Prototyping Toolkit We already know and love Camel JBang (if you don’t, check out Claus Ibsen’s YouTube channel for excellent tutorials).

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HOWTOS

Introduction In the previous blog posts (camel-tensorflow-serving and camel-torchserve), we discussed the recent release of Apache Camel 4.10 LTS, which introduced three new AI model serving components. 1 TorchServe component TensorFlow Serving component KServe component We previously wrote about the TorchServe and TensorFlow Serving components. This post introduces the KServe component, concluding the series. KServe Component KServe is a platform for serving AI models on Kubernetes. KServe defines an API protocol enabling clients to perform health checks, retrieve metadata, and run inference on model servers.

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AI

Observability is a pillar of any distributed Microservices oriented architecture. As the number of services to govern may rise in number, it’s very important to have a clear and easy way to understand (observe) what’s going on in a distributed system at any time. And this feature become even more important when you’re running your application in the cloud. What is Observability from Camel perspective The term Observability is often used with a wide perspective and may provide misunderstanding about what it really encompass.

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HOWTOS