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Intel’s joint venture to create machine learning with TensorFlow

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The open-source TensorFlow machine learning information center is improving faster than ever before, a great thanks to a collaboration between Google and Intel.

The open-source one API Deep Neural Network Library home-brewed by Intel is set as a default Library in TensorFlow, a joint venture mission helmed by Google. OneDNN is an open-source cross Platform Library of a particular conceptualization about deep learning of building blocks that are calculated for creators who has a coruscating color of both deep learning applications and TensorFlow.

TensorFlow has an out-and-out, pliable environment, several collections of tools, libraries, and community resources that pay a way for the researcher to keep the state-of-the-art ML, and developers can merely create and deploy ML-powered applications.

Moreover, it can depart from a simple and pliable architecture to new innovative ideas to code, to state-of-the-art models, and publication faster. ML production can happen anywhere; researchers could quickly train and deploy models in the cloud, on-prem, in the browser, or on-device.

There is no barrier to what language you use. According to Intel, the word of one for organizations and data analysts will witness a remarkable surge of up to 3 times performance for AI operations with TensorFlow. It will become one of the superlative open source machines.

The one library is part of a hefty strategy at Intel to help enable AI for developers, data scientists, researchers, and data engineers. To make it more effective across all the departments, Intel and Google could take this project to a great extent with collaboration.

According to reports, TensorFlow has managed to snatch a market share of 37%. Kaggle’s 2021 State of Data Science and Machine Learning survey understood TensorFlow’s usage at 53%.

However, while TensorFlow is popular technology, the one library and Intel’s approach to machine learning optimization isn’t just about TensorFlow. Intel’s approach toward machine learning won’t stop within limits. It can significantly boost the performance of machine learning.


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