Tensorflow 2.0 and Kubeflow for Scalable and Reproducable Enterprise AI


Romeo Kienzler1, 2, Holger Kyas2, 3, 4, 1IBM Center for Open Source Data and AI Technologies, USA, 2Berne University of Applied Sciences, Switzerland, 3Open Group, USA and 4Helvetia Insurance Switzerland, Switzerland


Towards the End of 2015 Google released TensorFlow 1.0, which started out as just another numerical library, but has grown to become a de-facto standard in AI technologies. TensorFlow received a lot of hype as part of its initial release, in no small part because it was released by Google. Despite the hype, there have been complaints on usability as well. Especially, for example, the fact that debugging was only possible after construction of a static execution graph. In addition to that, neural networks needed to be expressed as a set of linear algebra operations which was considered as too low level by many practitioners. PyTorch and Keras addressed many of the flaws in TensorFlow and gained a lot of ground. TensorFlow 2.0 successfully addresses these complaints and promises to become the go-to framework for many AI problems. This paper introduces the most prominent changes in TensorFlow 2.0 targeted towards ease of use followed by introducing TensorFlow Extended Pipelines and KubeFlow in order to illustrate the latest TensorFlow and Kubernetes ecosystem movements towards simplification for large scale Enterprise AI adoption.


Artificial Intelligence, TensorFlow, Keras, Kubernetes, KubeFlow, TFX, TFX Pipelines

Full Text  Volume 10, Number 1