NEO AI Software Pricing & Demo

Neo Artificial Intelligence Software is a framework for optimizing AI models, specifically their neural networks through data restructuring and memory use optimization. It is an open-source software offered by Amazon Web Services (AWS). Neo enables customers to run machine-learning training models across multiple operating environments. Amazon released the code as the Neo-AI Project under the Apache Software License. It’s basically a machine learning compiler and a runtime built on decades of research on traditional compiler technologies, such as LLVM and Halide.

NEO Pricing

The Neo Pricing project is an open-source project. Hence, the code is made available to the developer community for free on GitHub. Neo Pricing plans are available on the website. Detailed pricing for this company has not been disclosed, but it is in line with the leading competitors in the market. Most software companies and vendors require you to contact them with details so they can offer competitive personalized pricing based on your needs. For the best NEO pricing plans, contact the vendor.

NEO Demo

As it is open-source software, developers can test Neo Artificial Intelligence Software for free. Various guides and videos are also available online and on the Amazon Web Services (AWS) blog.

Features-

• Optimize and support popular AI models like TensorFlow, MXNet, PyTorch, ONNX, and XGBoost models.
• Supports various platforms from Intel, NVIDIA, ARM, Xilinx, Cadence, and Qualcomm.

• Optimize machine learning models for multiple hardware platforms without the need to tune models manually for each platform’s hardware and software configuration. 
• Support even edge devices, which tend to be constrained in computing power and storage.

• Increase efficiency by converting models into a common format to eliminate software compatibility problems

• Use a small fraction of the resources that a framework would typically consume by using a compact runtime.

• Integrate the custom code quickly into the compiler at the point at which it has the best effect on improving model performance.
• Enables device makers to customize the Neo-AI runtime for a particular software and hardware configuration of the device.