When Dillon Erb and Daniel Kobran met in 2014, they quickly realized that they shared a vision: GPUs would fuel a technology renaissance around parallel compute devices. Unfortunately, GPUs weren’t readily available in the cloud at the time.

Today, GPUs have taken over more and more applications. Yet, emerging workflows like deep learning that stood to gain the most from GPU acceleration, do not fit nicely into the web server model. Paperspace — which started off as an elegant front-end to GPU compute — has evolved as the platform to completely abstract infrastructure behind a simple software layer. As Dillon and Dan put it, they are setting out to make cloud ML as easy as building a modern web service.

So, why is this important?

AI is making its way into everything, defining new industries and creating new businesses. But there are development bottlenecks that could hinder innovation. A case in point: AI researchers and experts in areas like stats, TensorFlow, GANs, etc., can spend 95 percent of their time managing infrastructure instead of developing deep learning models. This is ludicrous considering AI research is one of the fastest growing professions. Just to get started with Deep Learning today, you are first required to set up infrastructure like Kubernetes, a job queuing system and tools to version data and models, among other painstaking tasks. This is fine for large companies like Google and Facebook that have the resources — — massive DevOps and software teams — to build sophisticated tools to solve this problem internally, but not ok for everyone else.

Paperspace’s newest product offering, Gradient°, is their most ambitious effort to date. They plan to put Facebook-grade AI tooling in the hands of every developer. In just one click, developers can now launch a GPU-enabled Jupyter Notebook, a powerful job runner, and a python module to run any code using the full power of the Paperspace GPU cloud. Think serverless, but for training deep learning models.

We want to live in a world where AI is not just reserved for large corporations. It’s imperative that everyone play a part in this movement. And, with AI increasingly emerging for important uses such as advancing cancer research, drug discovery and earthquake prediction, this couldn’t be more true.

We are excited to be one of the first backers Paperspace as they have emerged as a major player in the cloud ML/AI space. If you’re a developer and want to get started building AI applications check them out!