Customer services, like Pinterest, use deep learning for image recognition, curating rich custom experiences for its users. Financial businesses like PayPal are using NVIDIA GPU-accelerated deep learning for fraud detection. Consulting firm Accenture’s R&D arm and other businesses are using deep learning to detect Internet security threats. And healthcare is one of the most important industries for the application of AI and deep learning. This sweeping change in the landscape of data-driven business is taking place across all industries. It’s changing how farmers herd their animals and forcing us to rethink transportation. The first self-driving public transit system is already up and running, giving us a glimpse into futuristic cities that will exist within our lifetimes.
NVIDIA® Quadro® RTX™ and the GV100 bring AI to workstations; enabling new solutions such as big data analytics, DNN training and inferencing, and make ideal platforms to host the development of AL/DL/ML solutions to all industries and market segments requiring it. Deep learning is fueling all areas of business and relies on GPU acceleration, provided by the Tensor Cores featured in NVIDIA Quadro the RTX series and the GV100.
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Data science is one of the fastest growing fields of computer science, and it is affecting every industry. Companies in finance, insurance, retail and other areas are hiring data scientists, analysts and engineers at an unprecedented rate. To derive insights from their data that can transform their businesses, they need powerful hardware and software. NVIDIA accelerated data science solutions, built on NVIDIA CUDA-X AI, feature the RAPIDS suite of open source software libraries for data processing and machine learning, along with other data science software to maximize productivity, performance and ROI with the power of NVIDIA GPUs. NVIDIA-Powered Data Science Workstations reduce time to insight and lower the cost of data science projects with workstations built to ensure the highest level of compatibility, support and reliability.
GPU-accelerated analytics are invaluable when working with large volumes, high-velocity and new types of data that organizations are managing, and where an interactive (zero latency) analytics experience is required by data engineers, big data analysts and data scientists. Traditional analytics tools provide a click-and-wait experience for analysts, ranging from seconds to hours, depending on the dataset size and query complexity. GPU-accelerated analytics deliver speed-of-thought exploratory analysis for mission-critical use cases required to discover proverbial needle-in-a-haystack correlations that lead to insights. GPU-accelerated analytics software, from companies like OmniSci, take full advantage of NVIDIA Quadro RTX GPUs to deliver interactive analytics and insights that lead to competitive advantage or entirely new product or services concepts.
ON-DEMAND WEBINAR Learn how NVIDIA Quadro Powered Data Science Workstations and OmniSci are redefining speed and scale in Big Data Analytics and visualization by combining the fastest analytics software with the fastest hardware – NVIDIA Quadro RTX GPUs boosted by the NVIDIA NVLink interconnect.
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EPISODE 1 AI with the Heart of a Composer
EPISODE 2 Driving AI Innovation for the Long Haul
EPISODE 3 A New Weapon in the Fight Against Lung Cancer
EPISODE 4 Exploring New Looks in AI - ModiFace
EPISODE 5 On the Fast Track to AI-Powered Driving
EPISODE 6 Running Wild for Nature Conservation
EPISODE 7 Taking the Ice with AI
EPISODE 8 Taking AI to New Heights
EPISODE 9 A Bountiful Harvest
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? What’s the Difference Between Deep Learning Training and Inference?
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