On November 9, 2015, Google published a blog post titled “TensorFlow - Google’s latest machine learning system, open sourced for everyone,” written by Jeff Dean (Senior Google Fellow) and Rajat Monga (Technical Lead). The post announced that Google was releasing TensorFlow, the machine-learning system it had built and used internally, as free open-source software.
The announcement stated: “We’ve open-sourced TensorFlow as a standalone library and associated tools, tutorials, and examples with the Apache 2.0 license so you’re free to use TensorFlow at your institution (no matter where you work).” The Apache 2.0 license meant any company or researcher could use, modify, and ship the framework without paying Google or asking permission.
This mattered because, until then, the software that made deep learning practical - tools for building neural networks, running them on GPUs, and computing gradients automatically - lived mostly inside corporate research labs or fragmented academic projects. By open-sourcing a system used at Google scale, TensorFlow lowered the barrier to entry for the entire field and set off a wave of open framework competition. TensorFlow’s own site (tensorflow.org) became the central hub for its documentation and tutorials.