Intel has released the first set of open source AI reference kits specifically designed to make AI more accessible to organizations in on-prem, cloud and edge environments. First introduced at Intel Vision, the reference kits include AI model code, end-to-end machine learning pipeline instructions, libraries and Intel oneAPI components for cross-architecture performance.

Built in collaboration with Accenture, Intel’s AI reference kits enable data scientists and developers to learn how to deploy AI faster and more easily across healthcare, manufacturing, retail and other industries with higher accuracy, better performance and lower total cost of implementation.

Four kits are available for download today —  Utility asset health, Visual quality control, Customer chatbot, and Intelligent document indexing.

Utility asset health uses Intel-optimized XGBoost through the Intel oneAPI Data Analytics Library to model the health of utility poles with 34 attributes and more than 10 million data points.

Visual quality control: The AI Visual QC model was trained using Intel AI Analytics Toolkit, including Intel Optimization for PyTorch and Intel Distribution of OpenVINOTM toolkit, both powered by oneAPI to optimize training and inferencing to be 20% and 55% faster, respectively, compared to stock implementation of Accenture visual quality control kit without Intel optimizations for computer vision workloads across CPU, GPU and other accelerator-based architectures.

Customer chatbot includes deep learning natural language processing models for intent classification and named-entity recognition using BERT and PyTorch.  Intelligent document indexing was optimized with Intel Distribution of Modin and Intel Extension for Scikit-learn powered by oneAPI.

The kits are also available on Github.

Over the next year, Intel will release a series of additional open source AI reference kits with trained machine learning and deep learning models to help organizations of all sizes in their digital transformation journey.