Google built TensorFlow.js to bring machine learning to a broader group of developers, which has quickly embraced the technology.
In 2017, there were 2.3 million GitHub pull requests compared to 1 million for Python, Gupta and his team wrote in a technical paper supporting TensorFlow.js. And the open source TensorFlow.js code has been downloaded more than 300,000 times since its release last year, he said.
Running in the browser
Increasingly in AI, developers want to do more powerful things with browsers, such as speech recognition; image and object recognition; and pattern and anomaly detection. TensorFlow.js aims to put that power in the browser form factor without the need for additional cloud resources or specialized server or chipsets.
Ronald Schmelzeranalyst, Cognilytica
“TensorFlow.js is one more example of this, making TensorFlow-driven machine learning accessible to full-stack developers,” he said.
Server-side installation is not required. This lowers the threshold even further, which attracts even more front-end-centric developers to explore TensorFlow-based machine learning, Volk added.
He said he thinks this is just the beginning. Potential use cases for TensorFlow.js include the production of finished, off-the-shelf machine learning models that developers can directly embed in their applications for some quick and easy AI.