Algorithms aren’t always the only things in Artificial Intelligence. Some would argue that the data on which AIs are trained is more important than the models themselves, which is why IDC predicted that digital data will exceed 44 zettabytes by 2020. Thankfully, continued decline in cloud storage prices, cheaper media costs, better management tools, and innovations in object storage helped the rise of big data.
As all cloud storage providers aren’t created equal, some lack the fine-grain management tools required to collate, process, and transfer AI model data fast and efficient enough. And not all enterprises have storage stacks optimized for data science workflows.
Ontap AI, one stop solution to data management
Ontap AI, which is described as an “AI-proven architecture”, was announce today jointly by Nvidia and data storage company NetApp. Octavian Tanase, senior vice president at NetApp said that , it’s designed to help organizations achieve “edge to core to cloud” control over their data by delivering unprecedented access and performance using its powerful Nvidia’s DGX supercomputers and NetApp’s AFF A800 cloud-connected flash storage.
He told VentureBeat in a phone interview, “Our unique vision of a data pipeline [affords] simplicity of deployment. People are looking for scale, they want to start small and grow. At the end of the day, we want customers to be able to manage data across the edge, correlate datasets, build large data lakes, [and] ultimately make faster decisions and better decisions [about] data.”
Nvidia’s DGX-1 is at the core of Ontap AI, which is a second-generation deep learning-optimized server that sports up to 256B of GPU VRAM and multiple V100s GPUs. Nvidia claims, a single DGX-1 rack delivers petaflops of computing power, and can train neural machine translation models such as FairSeq in as less as a day and a half.
NetApp’s AFF A800s as mentioned before boast equally impressive performance: sub-200 microsecond latency and throughput of up to 300GB/s in a 24-node cluster.
Jim McHugh, vice president and general manager at Nvidia, said in an interview, “In the world of AI, data integration is essential. What’s really required for GPU AI training is quite different than traditional applications. The goal is to make it as painless as possible for data scientists, and as painless as possible for the people who build out infrastructure, too.”
One of Ontap AI’s first users is a U.K. based engineering consulting firm Cambridge Consultants. It applied Ontap AI in a health care vertical, where it’s leveraging the tech to build systems that evaluate drug treatments and their impacts on patient outcomes. It’s also used Ontap AI to create a deep learning program designed to learn how to paint like a human, Vincent.
IAS, Groupware Technology, ePlus, and WWT are the other Ontap AI launch partners.
Monty Barlow, head of AI at Cambridge Consultants, said, “Developing disruptive AI technology and turning this into breakthrough products and services for our customers is a vital requirement across many markets we work in. [It’s] simplifying and accelerating the data pipeline for deep learning.”
Nvidia’s powerful hardware platform is a big help for NetApp, which reported net revenue of $5.9 billion in May for its financial year 2018. Amazon Web Services, Microsoft Azure, and Google Cloud, have also partnered with this storage vendor.