Floyd’s Infrastructure runs entirely on Docker. That makes it backend agnostic (our cloud offering currently runs on AWS). Floydhub uses nvidia-docker for the deep learning jobs that require GPU. We also version the entire pipeline (code, data, params and environment) for exact reproducibility.

GPUs instances are really expensive. One of the biggest challenges at the moment is around reducing this cost. Eg. Spot Instances and Spot Blocks. Still some challenges to be solved there.

We also want Floyd to be an end-to-end solution for building, training and deploying deep learning models. In that vein, we are also investing in adding support for Tensorflow serving but it has been a rough ride so far. Getting a generic solution that can host any Tensorflow model has not been straightforward.

ML-as-a-service offered by many companies (Microsoft Cognitive Services, Google Cloud Prediction, IBM Watson, etc.) are fairly similar. They're great out-of-the-box for some domains, say English speech recognition. For others (text/image), they’re fairly easy to get started with (don’t need much training data, no managing infra, etc.) However, they are mostly black boxes and set a slightly low bar in terms of quality. Anyone doing serious AI will hit the limits of what they offer fairly quickly.

The DL community is awesome in its openness and contributions. Our goal with FloydHub, in contrast to the ML APIs, is to provide the tools for data scientists to effectively leverage this. We want to solve the engineering hurdles that come in the way of doing some cool science.