"The difference in a successful machine learning project often boiled down to how well the gathering and use of labeled data was executed and managed.
Google Brain, an artificial intelligence and machine learning project at Google, has been used to power services like Android's speech recognition system and photo search on Google+.
We continue to believe there is a huge opportunity with autonomous systems, that Apple has unique capabilities to contribute, and that this is the most ambitious machine learning project ever," a spokesperson said.
While TensorFlow has only been available for a little over a year, it has quickly become the most popular open source machine learning project on GitHub.
In a recent interview with TechCrunch, David Aronchick, who runs the open source Kubeflow Kubernetes machine learning project at Google, was running Kubernetes in the early days.
First, the increasing complexity of machine learning project requirements as well as of the data itself may require different types of solutions.
"We continue to believe there is a huge opportunity with autonomous systems, that Apple has unique capabilities to contribute, and that this is the most ambitious machine learning project ever".
David Aronchick, who now runs the open source Kubeflow Kubernetes machine learning project at Google, was running Kubernetes in the early days (way back in 2015) and he was certainly surprised to see how big it has become in such a short time.
The downside that's commonly cited when referring to FPGA is that it is a niche piece of hardware that is complex to calibrate and modify, as well as expensive, making it less of a cover-all solution for machine learning projects.
