10 months ago, I was working at Microsoft and doing a lot of deep learning (DL) there. While the DL community is terrific, I was often frustrated by how difficult it was to get started and build upon others’ work. For example, running any popular Github project often started with an exercise in dependency hell. As I untangled these for myself, I wrote up some notes on setting up popular DL frameworks, which unexpectedly started trending on HN after someone posted it there (https://news.ycombinator.com/item?id=11697571). That's when I realized that engineering was a huge bottleneck in deep learning and a problem worth solving after all.

I’ve since quit my job and have been working fulltime for the last 9 months on building FloydHub to make deep learning easier. Our goal is to let the data scientists focus on the science, while we handle the engineering grunt work (provisioning and scaling infra, running reproducible experiments, enabling sharing and collaboration, supporting DL frameworks with zero setup, shipping trained models to production easily, etc.) Lots of interesting challenges - happy to talk about them!

We have a lot of work ahead, but we’re excited to share with you what we have so far! Looking forward to your feedback.