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Software Engineering Daily · Nov 9, 2021 · 48 min

Learning Tensorflow.js with Gant Laborde

Machine learning models must first be trained. That training results in a model which must be serialized or packaged up in some way as a deployment artifact. A popular deployment path is using Tensorflow.js to take advantage of the portability of JavaScript, allowing your model to be run on a web server or client. Gant Laborde is Chief Innovation Officer at Infinite Red, a React Native consulting team and the author of Learning TensorFlow.js: Powerful Machine Learning in JavaScript from O’Reilly. In this interview, we explore use cases for Tensorflow.js. Sponsorship inquiries: sponsor@softwareengineeringdaily.com

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Machine learning models must first be trained. That training results in a model which must be serialized or packaged up in some way as a deployment artifact. A popular deployment path is using Tensorflow.js to take advantage of the portability of JavaScript, allowing your model to be run on a web server or client.

Gant Laborde is Chief Innovation Officer at Infinite Red, a React Native consulting team and the author of Learning TensorFlow.js: Powerful Machine Learning in JavaScript from O’Reilly. In this interview, we explore use cases for Tensorflow.js.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com