According to a recent research report by MarketsandMarkets, “Deep Learning Market by Offering (Hardware, Software, and Services), Application (Image Recognition, Signal Recognition, Data Mining), End-User Industry (Security, Marketing, Healthcare, Fintech, Automotive, Law), and Geography – Global Forecast to 2023,” the deep learning market, the overall deep learning market is estimated to be valued at $3.18 billion in 2018 and is expected to be worth $18.16 billion by 2023.
Improving computing power, declining hardware cost, and the increasing adoption of cloud-based technology are fueling the growth of the deep learning market. Usage in big data analytics and growing AI adoption in customer-centric services are the other key factors driving this market.
Computer and server manufacturer Inventec Enterprise Business Group (Inventec EBG) is taking advantage of the expanding market opportunities by offering its P47G4 server solution, which has been optimized for AMD deep learning technologies. These technologies provide customers with solutions for quick deep learning project deployments.
The P47G4 server is one of four optimized server solutions and features a 2U, single-socket system equipped with AMD EPYC processors and up to four AMD Radeon Instinct MI25 GPU accelerators.
The combination of world-class servers, high-performance AMD Radeon Instinct GPU accelerators and the AMD ROCm open software platform, with its MIOpen deep learning libraries, provides easy-to-deploy, pre-configured solutions for leading deep learning frameworks, enabling researchers, scientists and data analysts to accelerate discovery.
“The Inventec P47G4 is a perfect, compact solution to accelerate machine learning discovery on pre-configured, ready-to-deploy AMD Radeon Instinct GPU-powered servers with the ROCm open software platform and leading deep learning frameworks, including TensorFlow,” says Jack Tsai, General Manager of Inventec Enterprise Business Group.
With the AMD Radeon Instinct MI25 accelerator, the Inventec P47G4 server meets the emerging scale-out demands of datacenters. In addition, with the increasing utilization demands of AI in high performance computing (HPC) markets, it enables organizations to find the right fit for key datacenter/HPC workloads with a low total cost of ownership.