SFL Scientific Announces Deep Learning Services Partnership with NVIDIA

BOSTON, MA., October 6, 2017 – SFL Scientific today announced a new business relationship with NVIDIA focused on extending services for businesses that are capitalizing on the significant growth opportunities created by GPU-accelerated computing and machine learning technology.

Through the NVIDIA Partner Network, the companies will work together to provide integrated solutions and professional services that take advantage of NVIDIA’s GPU computing platform portfolio, focusing on enterprise software development and engineering, allowing businesses to accelerate adoption. SFL Scientific will provide custom algorithm development to assist data scientists, engineers, and business leaders to better analyze and manage their mounting data volumes, build new frameworks, and help make data-driven decisions.

The collaboration will feature initiatives that expand capabilities for the integration and deployment of NVIDIA GPU computing solutions, including NVIDIA DGX systems, with the initial phase focused on providing services for deep learning and neural network development for image analysis, natural language processing, and time-series analysis.

“The combination of SFL Scientific’s data science and NVIDIA’s industry-leading GPU computing platform will benefit not only our respective clients, but also communities eager to reap the results of automation, such as for medical image analysis in healthcare,” said Daniel Ferrante, Chief Data Officer at SFL Scientific. “We’re excited about providing rapid development and technical innovations that this relationship is being designed to foster, incorporating advanced machine learning and deep learning capabilities, with the processing might of NVIDIA’s GPU and DGX systems.”

“Partners like SFL Scientific are helping us achieve our goal of democratizing AI,” said Craig Weinstein, Vice President of the Americas Partner Organization at NVIDIA. “They understand the complexities of using AI computing to solve real business problems, and can help customers leverage NVIDIA’s AI platforms to deliver tangible business benefits.”

As a technical consultant, SFL will give customers and developers a fast on-boarding ramp to data science capabilities and building the desired tools and environments. SFL will offer initial strategy, data ingestion and warehousing, stream processing, and algorithm development for analytics, helping to collect, curate, analyze, and productionize systems across data center and cloud.

“Through this partnership we will support NVIDIA GPU computing and provide an integrated approach to open data science and machine learning, allowing teams to easily seek expert help and operationalize their systems,” said Michael Segala, CEO, SFL Scientific. “By incorporating advanced AI and deep learning capabilities with industry-leading hardware, we can help clients achieve powerful processing and analytics results at scale, creating smarter and more agile businesses.”

Through its data consulting and custom development strategy, SFL Scientific continually expands its offering by working closely with industry leaders, taking full advantage of trends and best practices to accelerate business outcomes. "We're excited to further our ability to educate customers, provide return on hardware, and leverage their data as we drive value through new systems,” said Michael Segala.

 

About SFL Scientific
SFL is a US-based data science consulting and services company, providing a broad range of solutions in data strategy, data engineering, big data, machine learning development, and AI. SFL works at the intersection of business and technology to help clients improve their performance and create sustainable value for stakeholders using custom algorithm development and integrating data-driven solutions. We are industry agnostic and instead focus on solving complex, R&D, and core business problems.

For more information, please visit sflscientific.com and connect with SFL on LinkedIn and Twitter

Media Contact:

SFL Scientific
Stephen Savoia
ssavoia@sflscientific.com