saip

Thanks! We've been iterating on it for a while. My sister is a UX designer and helped a ton. I still think there's room for improvement, but it's great to hear a positive comment about it!

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.

I assume you're talking about AzureML Studio. It's a pretty neat UI-centric tool for building machine learning workflows! It's great if you're starting out with ML, but offers little in terms of customizability. For example, it only supports R and Python, has no GPUs, no CLI, no container support for managing reproducible environments, etc. I think these are kind of deal breakers for doing deep learning :)

FWIW, I worked as a data scientist in Bing for 6 years and haven't seen/heard any other data scientist use it internally. We ended up building our own GPU clusters and going through the regular drill.

Agreed. The tooling around deep learning is not as mature as the tooling around software development. There is a fair amount of engineering and grunt work needed to even get started, let alone build on others' research. A few problems from top of mind: - Setup: Installing DL frameworks, Nvidia drivers and CUDA is an exercise in dependency hell. Trying to run someone's project, which has different dependencies than what you have is difficult to get right. Docker images [1] and nvidia-docker make this simple, but are still not the norm. - Reproducibility: This is big as Denny mentions. Folks still use Github for sharing code. But DL pipelines need versioning of more than just code. It's code, environment, parameters, data and results. - Sharing and collaboration: I've noticed that most collaboration on deep learning research, unlike software, happens only when the folks are co-located (e.g. part of the same school or company). This likely links back to reproducibility, but there are not many good tools for effective collaboration currently IMHO. [1] https://github.com/floydhub/dl-docker (Disclaimer: I created this)

Agreed. The tooling around deep learning is not as mature as the tooling around software development. There is a fair amount of engineering and grunt work needed to even get started, let alone build on others' research. A few problems from top of mind:

- Setup: Installing DL frameworks, Nvidia drivers and CUDA is an exercise in dependency hell. Trying to run someone's project, which has different dependencies than what you have is difficult to get right. Docker images [1] and nvidia-docker make this simple, but are still not the norm.

- Reproducibility: This is big as Denny mentions. Folks still use Github for sharing code. But DL pipelines need versioning of more than just code. It's code, environment, parameters, data and results.

- Sharing and collaboration: I've noticed that most collaboration on deep learning research, unlike software, happens only when the folks are co-located (e.g. part of the same school or company). This likely links back to reproducibility, but there are not many good tools for effective collaboration currently IMHO.

[1] https://github.com/floydhub/dl-docker (Disclaimer: I created this)