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

Introduction As noted in the previous article, the recent release of Apache Camel 4.10 LTS introduced three new AI model serving components into its supported component family. 1 TorchServe component TensorFlow Serving component KServe component Previously we wrote about the TorchServe component, this time we introduce the TensorFlow Serving component. TensorFlow Serving component TensorFlow Serving is the serving feature provided by the popular machine learning framework TensorFlow. By using the Camel TensorFlow Serving component, you can invoke AI models deployed on the TensorFlow Serving model servers through their gRPC Client APIs.

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AI

Electronic Data Interchange (EDI) underpins the flow of information in numerous industries. From healthcare, retail, and aviation, to finance, manufacturing, and logistics, EDI is the workhorse carrying billions of transactions across applications in these industries. Historically viewed as a long, complex and costly journey, connecting EDI to the enterprise is traditionally thought to belong in the realm of expensive proprietary software or organisations with sizeable in-house IT teams. The goal of this blog post is to dispel this perception.

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