Intel Released Free Open Source AI Reference Kits to Make Artificial Intelligence More Accessible

Artificial Intelligence is finding its applications across industries and while big IT services providers have built their capabilities for providing AI solutions to its cliemts, its not an easty task for small-scale companies or freelance developers. Companies and developers are looking to infuse AI into their solutions and the reference kits contribute to that goal.

Intel, in partnership with Accenture, 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. Intel has launched a series of trained AI reference kits to the open source community to help enterprises innovate and accelerate their digital transformation journey.

These 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.

The reference kits are open source, pre-built AI with meaningful enterprise contexts for both greenfield AI introduction and strategic changes to existing AI solutions. 

Built on the foundation of the oneAPI open, standards-based, heterogeneous programming model, these kits are also available on Github. It include AI model code, end-to-end machine learning pipeline instructions, libraries and Intel oneAPI components for cross-architecture performance. Four kits are available for download, described below -
  1. Utility asset health:
    For — Energy consumption, Power Distribution

    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.

    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, like new pole manufacturer, outages and other changes in condition, are provided.

  2. Visual quality control:
    For — Quality control (QC) in any manufacturing operation.

    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 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.

  3. Customer chatbot:
    For —  Conversational chatbots.

    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.

  4. Intelligent document indexing:
    For — Analyzing Mass documents

    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 stock implementation of Accenture Intelligent document indexing kit without Intel optimizations4 for reviewing and sorting the documents at 65% accuracy.
Intel has planned over 30 AI reference kits with trained machine learning and deep learning models for release to the open source community. Each kit includes model code, training data, instructions for the machine learning pipeline, libraries, and Intel® oneAPI components.

Post a Comment

أحدث أقدم
Like this content? Sign up for our daily newsletter to get latest updates.