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The surface area where native code = more security flaws (mainly in server side networked applications). Java with Java's robust server side application development (Vert.x on top of netty)Īllows for better access to faster math code in production while minimizing With native math libraries such as TensorFlow and our very own DL4J'sĪt the core of Konduit Serving are the JavaCPP Presets,Ĭombining JavaCPP's low-level access to C-like APIs from Konduit Serving was built with the goal of providing proper low level interoperability Machine learning pipelines from pre-processing to model serving, exposable Provides building blocks for developers to write their own production Keras, Deeplearning4j (DL4J) or PMML models, use ModelStep.įunctionality for other pre-processing tasks, such as DataVec transform processes or image transforms. To perform inference on a (mix of) TensorFlow, Such as labels in a classification example.įor instance, if you want to run arbitrary Python code for pre-processing purposes, Transforming the output in a way that can be understood by humans,.
The core abstraction is an idea called a "pipeline step".Īn individual step is meant to perform a task as part of using a machine learning Konduit Serving is a serving system and framework focused on deploying machine learning Konduit Serving: Enterprise Runtime for Machine Learning Models
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