TieSet Inc. Joins NVIDIA Inception
TieSet Inc. has joined NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data science.
TieSet Inc. today announced it has joined NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data science.
TieSet is focused on helping big data AI systems solve the enormous challenges of privacy, power consumption, storage, and computational costs by using distributive artificial intelligence.
Read More: Instruqt Announces Record Year As Demand For Growth-Focused Virtual IT Labs Surges
NVIDIA Inception will allow TieSet to help drive its business forward through go-to-market support, training, and technology assistance. It will also offer TieSet the opportunity to collaborate with industry-leading experts and other AI-driven organizations.
“We’re very excited to share our new federated learning-based technology platform with the world, and we know that working with partners like NVIDIA will be essential to this process,” says Kiyoshi Nakayama, TieSet Founder and CEO.
NVIDIA Inception helps startups during critical stages of product development, prototyping, and deployment. Every NVIDIA Inception member gets a custom set of ongoing benefits, such as NVIDIA Deep Learning Institute credits, marketing support, and technology assistance, which provides startups with the fundamental tools to help them grow.
Read More: SalesTechStar Interview with Gemma Cipriani-Espineira, Chief Customer Officer at Chili Piper
TieSet is a Silicon Valley-based startup focusing on the transition from big data to collective intelligence, by enhancing intelligence without invasion. TieSet’s federated learning platform, called STADLE (Scalable Traceable Adaptive Distributed Learning), is a low-code, user-friendly, SaaS platform used easily across multiple industries including financial, healthcare, and others. STADLE provides solutions for the business challenges of data privacy, data accessibility, and model accuracy.
