Intel releases Open Source Artificial Intelligence Reference Kits

Intel has released the 1st set of open source AI reference kits specifically designed to make AI more accessible within organizations.

First introduced at Intel Vision 2022, these reference kits make AI more accessible, whether in on-prem, cloud or edge environments. These AI kits include:  

• AI model code,
• End-to-end machine learning pipeline instructions,
• Libraries and intel oneAPI components for cross-architecture performance.

The Intel innovative ecosystem is designed to help data scientists and developers 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.

There are 4 kits available for download today:

1) Utility asset healt

This predictive analytics model was trained to help utilities deliver higher service reliability. Plus 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. The data includes asset age, mechanical properties, geospatial data, inspections, manufacturer, prior repair and maintenance history, and outage records. The predictive asset maintenance model continuously learns as new data such as new pole manufacturer, outages and other changes in condition are provided.

2) Visual Quality Control

Quality control (QC) is essential in any manufacturing operation. And the challenge with computer vision techniques is that they often require heavy graphics compute power during training and frequent retraining as new products are introduced. 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 optimizations 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.

3) Customer Chatbot

Conversational chatbots have become a critical service to support initiatives across the enterprise. And 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 optimizations – across heterogeneous architectures, and allow developers to reuse model development code with minimal code changes for training and inferencing.

4) Intelligent Document Indexing

Enterprises process and analyze millions of documents every year and many of the semi-structured and unstructured documents are routed manually. And 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. These tools improve data pre-processing, training, and inferencing times to be 46%, 96%, and 60% faster, respectively, compared to the stock implementation of Accenture Intelligent document indexing kit without Intel optimizations4 for reviewing and sorting the documents at 65% accuracy.

« Innovation thrives in an open, democratized environment. The Intel accelerated open AI software ecosystem including optimized popular frameworks and Intel’s AI tools are built on the foundation of an open, standards-based, unified oneAPI programming model. These reference kits, built with components of Intel’s end-to-end AI software portfolio, will enable millions of developers and data scientists to introduce AI quickly and easily into their applications or boost their existing intelligent solutions. »

– Wei Li, Ph.D., Intel vice president and general manager of AI and Analytics

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https://pulse2.com/intel-intc-releases-open-source-ai-reference-kits