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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USECASESHOWTOSTOOLING

Introduction This has been several blog posts now where we have learned about how to use generative AI for data extraction from a Camel route. Starting from the initial inception, we have then focused a lot on how to best combine Camel and Quarkus LangChain4j. In this blog post, we will reap the benefit of this great combination to improve the accuracy of our data extraction almost for free. Almost for free really?

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AI