AI development has become a core part of modern software engineering, and NVIDIA is committed to finding ways to bring optimized accelerated computing to every developer that wants to start experimenting with AI. To address this, we��ve been working on making the accelerated computing stack more accessible with NVIDIA Launchables: preconfigured GPU computing environments that enable you to��
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NVIDIA AI Workbench is now in beta, bringing a wealth of new features to streamline how enterprise developers create, use, and share AI and machine learning (ML) projects. Announced at SIGGRAPH 2023, NVIDIA AI Workbench enables developers to create, collaborate, and migrate AI workloads on their GPU-enabled environment of choice. To learn more, see Develop and Deploy Scalable Generative AI Models��
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With NVIDIA TAO Toolkit, developers around the world are building AI-powered visual perception and computer vision applications. Now the process is faster and easier than ever, thanks to significant platform enhancements and strong ecosystem adoption. NVIDIA TAO Toolkit supports more than 10 computer vision and vision AI modalities, including image classification, object detection��
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Diamond Light Source is a world-renowned synchrotron facility in the UK that provides scientists with access to intense beams of x-rays, infrared, and other forms of light to study materials and biological structures. The facility boasts over 30 experimental stations or beamlines, and is home to some of the most advanced and complex scientific research projects in the world. I08-1��
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Learn how game developers can add leading-edge NVIDIA RTX technologies to Unreal Engine with custom branches.
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Data is the lifeblood of AI systems, which rely on robust datasets to learn and make predictions or decisions. For perception AI models specifically, it is essential that data reflects real-world environments and incorporates the array of scenarios. This includes edge use cases for which data is often difficult to collect, such as street traffic and manufacturing assembly lines.
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Crossing the chasm and reaching its iPhone moment, generative AI must scale to fulfill exponentially increasing demands. Reliability and uptime are critical for building generative AI at the enterprise level, especially when AI is core to conducting business operations. NVIDIA is investing its expertise into building a solution for those enterprises ready to take the leap.
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Imagine you are a robotics or machine learning (ML) engineer tasked with developing a model to detect pallets so that a forklift can manipulate them. ?You are familiar with traditional deep learning pipelines, you have curated manually annotated datasets, and you have trained successful models. You are ready for the next challenge, which comes in the form of large piles of densely stacked��
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Data labeling and model training are consistently ranked as the most significant challenges teams face when building an AI/ML infrastructure. Both are essential steps in the ML application development process, and if not done correctly, they can lead to inaccurate results and decreased performance. See the AI Infrastructure Ecosystem of 2022 report from the AI Infrastructure Alliance for more��
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Generative AI has marked an important milestone in the AI revolution journey. We are at a fundamental breaking point where enterprises are not only getting their feet wet but jumping into the deep end. With over 50 frameworks, pretrained models, and development tools, NVIDIA AI Enterprise, the software layer of the NVIDIA AI platform, is designed to accelerate enterprises to the leading edge��
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