Intel

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Many AI applications have data pipelines that include several processing steps when executing inference operations. These steps can include data transformation, inference execution and conditional processing. The Seldon Core* open source machine learning framework facilitates management of

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. One of the biggest challenges to AI can be eliciting high-performance deep learning inference that runs at real-world scale, leveraging existing infrastructures. Combining efficiency and flexibility, the Intel® Distribution of OpenVINO™ toolkit (a developer tool suite that

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Data scientists need annotated data (and lots of it) to train the deep neural networks (DNNs) at the core of AI workflows. Obtaining annotated data, or annotating data yourself, is a challenging and time-consuming process. For example,

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Deep learning framework optimizations and tools that streamline deployment are advancing the adoption of inference applications on Intel® platforms. Reducing model precision is an efficient way to accelerate inference on processors that support low precision math, with