Considering TensorFlow for the Enterprise

Introduction

This report examines the TensorFlow library and its ecosystem from the perspective of the enterprise considering the adoption of deep learning in general and TensorFlow in particular. Many enterprises will not be jumping into deep learning “cold” but will instead consider the technology as an augmentation or replacement of existing data analysis pipelines. What we have found is that the decision to use deep learning sets off a branching chain reaction of additional, compounding decisions. In considering this transition, we highlight these branches and frame the options available, hopefully illuminating the path ahead for those considering the journey. More specifically, we examine the potential rationales for adopting deep learning, examine the various deep learning frameworks that are available, and, finally, take a close look at some aspects of TensorFlow and its growing ecosystem.

Due to the popularity of TensorFlow, there is no shortage of tutorials, reports, overviews, walk-throughs, and even books (such as O’Reilly’s own Learning TensorFlow or TensorFlow for Deep Learning) about the framework. We will not go in-depth on neural network basics, linear algebra, neuron types, deep learning network types, or even how to get up and running with TensorFlow. This report is intended as an overview to facilitate enterprise learning and decision making.

We provide this information from both a high-level viewpoint and also two different enterprise perspectives. One view ...