Intel released the first set of open source AI reference kits, including AI model code, end-to-end machine learning pipeline instructions, libraries and Intel oneAPI components, for on-prem, cloud and edge environments.
Intel said its 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:
- Utility asset health: This predictive analytics model was trained to help utilities deliver higher service reliability. It 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 OpenVINO 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 optimizations2 for computer vision workloads across CPU, GPU and other accelerator-based architectures. Using computer vision and SqueezeNet classification, the AI Visual QC model used hyperparameter tuning and optimization to detect pharmaceutical pill defects with 95% accuracy.
- Customer chatbot: Conversational chatbots have become a critical service to support initiatives across the enterprise. AI models that support conversational chatbot interactions are massive and highly complex. This reference kit includes deep learning natural language processing models for intent classification and named-entity recognition using BERT and PyTorch. Intel Extension for PyTorch and Intel Distribution of OpenVINO toolkit optimize the model for better performance – 45% faster inferencing compared to stock implementation of Accenture customer chatbot kit without Intel optimizations3 – across heterogeneous architectures, and allow developers to reuse model development code with minimal code changes for training and inferencing.
- Intelligent document indexing: Enterprises process and analyze millions of documents every year, and many of the semi-structured and unstructured documents are routed manually. AI can automate the processing and categorizing of these documents for faster routing and lower manual labor costs. Using a support vector classification (SVC) model, this kit was optimized with Intel Distribution of Modin and Intel Extension for Scikit-learn powered by oneAPI.
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