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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CAMELAI

© rawpixel.com Apache Camel community is happy to announce the general availability of Camel K 2.6.0. We have a lot of new exciting features we want to share within this release. Plain Camel Quarkus runtime From version 2.6.0 onward, you’ll be able to run plain Camel Quarkus. You may know that since the beginning, Camel K had used Camel K Runtime, a lightweight runtime built on top of Camel Quarkus. In order to avoid introducing any breaking compatibility change, you will need to configure each of your Integrations (or the IntegrationPlatform) to be able to use this runtime.

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RELEASESCAMEL KROADMAP

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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USECASESHOWTOSCAMELJBANGTRANSFORMATION