Core Scientific and SQream Team Up to Accelerate Massive Data Analytics on GPU Cloud for Data Scientists

Core Scientific and SQream Team Up to Accelerate Massive Data Analytics on GPU Cloud for Data Scientists

Core Scientific, the leader in best-in-class artificial intelligence and blockchain cloud infrastructure and software solutions, and SQream, a leader in GPU-accelerated analytics, today announced a partnership to bring accelerated analytics of massive data to the GPU Cloud for Data Scientists. This partnership provides cost effective accelerated analytics based on GPU computing for machine learning and deep learning used by telecom operators, financial services, healthcare organizations, pharmaceutical companies and more.

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Core Scientific is a leader in artificial intelligence and blockchain technologies, delivering best-in-class infrastructure and software solutions. Announced earlier this year, the Cloud for Data Scientists brings the ease of the public cloud with the myriad of benefits of co-location. SQream DB enables enterprises to quickly and easily load massive volumes of data in the range of terabytes to petabytes for analysis, while generating higher quality business intelligence faster than any other data store at these volumes.

“SQream running on the Cloud for Data Scientists empowers customers to turns massive datasets into more comprehensive and timely insights that have a direct impact on business performance,” said Ian Ferreira, Chief Product Officer, Core Scientific. “This new offering is proof of our commitment to empower data scientists to take on the most challenging problems by partnering with leaders in GPU hardware and software.”

“The availability of the SQream DB on Core Scientific’s near-cloud infrastructure will provide data science customers with the excellent performance, scale and functionality needed to compete and grow their businesses,” said Ami Gal, CEO, SQream. “This partnership will make SQream DB’s accelerated analytics platform accessible to more organizations, enabling them to rapidly analyze significantly more data for better decision making insights.”

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