Core Scientific
Unlocking Best-In-Class AI Applications

In an increasingly distributed and digital world, organizations are adopting hybrid- and multicloud management solutions to optimize performance and decrease costs. Forward-thinking IT leaders are deploying artificial intelligence (AI) models to automatically leverage the extensive data sets created by tech-driven deployments for the identification, analysis, and management of potential operational risks. However, as more enterprises try to scale their AI applications, the ever-increasing data and enhanced workloads are hindering data scientists in training and inferencing their AI models. In firms trying to leverage AI at scale for their multi-cloud environment, data science teams have to share information and resources across disparate compute environments, which more often than not, surpass their existing capacities and cause inefficiencies in AI model training and inferencing. Alternatively, the surge in data sets generated by AI scaling also exceed the performance capabilities of deployed GPUs, stymieing data scientists from performing their data science tasks. Addressing these challenges with its innovative Core Scientific Plexus software, is Washington-based Core Scientific. “Our hybrid- and multi-cloud offering is specially designed to help enterprises overcome the hurdles on their path to leveraging AI at scale,” says Ian Ferreira, chief product officer of AI at Core Scientific.

Founded in 2017 with a goal to become the premier AI provider, Core Scientific currently offers its own cloud environment—Core Scientific’s Cloud for Data Scientists (C4DS)—with its Plexus software stack to unlock newer AI and ML applications. The company’s C4DS platform comprises best-in-class hardware components and, unlike public clouds, does not offset customer value through virtualization and high fabric overhead. Moreover, C4DS offers access to an AI PaaS, enabling users to seamlessly use its capacities for high-speed networking and storage, as well as efficient and quick architecture designing for high-performance computing (HPC).

As a result, the C4DS is highly effective in helping data scientists bring their data into Core Scientific’s cloud experience with the efficiencies of co-located data lakes.

Simultaneously, the company’s Plexus software stack allows data scientists to run a private cloud deployment to take advantage of their existing infrastructure and machine learning-powered processes. Core Scientific’s software also simplifies the inferencing of AI models across many edge devices, and aids data scientists in aligning model versions with the equipment they are being deployed on. Above all, the company offers its SaaS and PaaS offerings through a conventional subscription model, allowing clients to get onboard in a quick, efficient, and in a familiar manner. Core Scientific also provides models that monitor and recommend the best-suited and the most cost-effective cloud environments to help its clients take advantage of the different pricing and node availability across the major public clouds. “Our recommendation engine removes the friction of being multi-cloud and helps customers perform HPC with ease,” adds Ferreira.

With such unparalleled capabilities, Core Scientific has ignited several success stories across multiple market sectors since its inception. In one instance, the company helped a pharmaceutical company gain performance boosts of up to 60 percent by assisting them in transitioning their deep learning models from a public cloud onto C4DS. “In this project, we not only helped them unlock newer HPC applications but also eliminated the disproportionate consumption of cloud resources that they were facing,” states Ferreira.

Moving forward, Core Scientific plans to enter the fleet management space and help organizations manage their AI edge devices better by driving the value of C4DS and Plexus over the existing public cloud deployments. “We will continue building out our SaaS and PaaS offerings to capitalize on the upcoming surge in the number of organizations looking to scale their AI endeavors,” concludes Ferreira.