Nvidia offers a range of AI workflows to cater to different needs and use cases. Let's take a look at some of the AI workflows offered by Nvidia.
1. Data Science Workflows: Nvidia provides comprehensive data science workflows that enable data scientists to iterate quickly through the model-building process. It offers a range of tools and libraries such as NVIDIA RAPIDS, cuDF, cuML, and cuGraph that allow data scientists to load, clean, transform, and analyze data at scale. These workflows help data scientists to build and deploy powerful AI models with ease.
2. Deep Learning Workflows: Nvidia's deep learning workflows provide a scalable, efficient, and easy-to-use environment for building and training deep neural networks. It offers popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet, as well as pre-trained models that can be easily customized to fit specific use cases.
3. Inference Workflows: Nvidia's inference workflows enable developers to deploy AI models at scale. It offers a range of optimized inference engines such as TensorRT, Triton Inference Server, and DeepStream SDK that deliver high performance and low latency, even on edge devices.
4. Autonomous Vehicle Workflows: Nvidia's autonomous vehicle workflows provide a comprehensive development platform for building self-driving car applications. It offers a range of tools and libraries such as DRIVE Constellation, DRIVE AV, and DRIVE IX that enable developers to build and test autonomous vehicle software and hardware components.
In conclusion, Nvidia offers a range of AI workflows that cater to different needs and use cases. Its comprehensive data science, deep learning, inference, and autonomous vehicle workflows provide developers with the tools and libraries they need to build and deploy powerful AI models with ease.
