In 2015 when we added Paperspace to our portfolio, it was offering full computer in the cloud accessible through any web browser. By “full”, we really mean it because you could play games and perform heavy duty jobs like using 3D CAD softwares, rendering 3D simulations and video editing etc. However, now Paperspace has walked an extra mile by offering GPU-powered cloud infrastructure for machine learning and data science.
Their new product, Gradient running on Paperspace cloud makes it possible with a suite of tools (like 1-Click Jupyter Notbooks, Job runner and Python module etc.) for data scientists to just focus on code and building models leaving rest of the management to Gradient. Not just that, advanced GPUs offered by Paperspace for machine learning, are more powerful yet less expensive than cloud giants like AWS, GCP and Azure.
With machine learning going mainstream these days, this new direction of Paperspace is quite strategic for their future growth and not just us but other investors also validate it by providing $13M in Series A funding to Paperspace.
Best of luck to Paperspace with finding their own space in the world of data science!
Battery Ventures led the round with participation from SineWave Ventures, Intel Capital and Sorenson Ventures. Existing investor Initialized Capital also participated. Today’s investment brings the total amount to $19 million raised.
Dharmesh Thakker, a general partner with Battery Ventures sees Paperspace as being in the right place at the time. As AI and machine learning take off, developers need a set of tools and GPU-fueled hardware to process it all. “Major silicon, systems and Web-scale computing providers need a cloud-based solution and software ‘glue’ to make deep learning truly consumable by data-driven organizations, and Paperspace is helping to provide that,” Thakker said in a statement.
Paperspace provides its own GPU-powered servers to help in this regard, but co-founder and CEO Dillon Erb says they aren’t trying to compete with the big cloud vendors. They offer more than a hardware solution to customers. Last spring, the company released Gradient, a serverless tool to make it easier to deploy and manage AI and machine learning workloads.
“It’s really a greenfield opportunity, and we want to be the go-to platform that you can start building out into intelligent applications without thinking about infrastructure.” With $13 million in hand, it’s safe to say that they are on their way.
Please read the full story at TechCrunch.