Intel Releases Open Source AI Reference Kits
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. 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.
“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” stated Wei Li, Ph.D., Intel vice president and general manager of AI and Analytics.
AI Reference Kits
AI workloads continue to grow and diversify with use cases in vision, speech, recommender systems and more. Intel’s AI reference kits, built in collaboration with Accenture, are designed to accelerate the adoption of AI across industries. They are open source, pre-built AI with meaningful enterprise contexts for both greenfield AI introduction and strategic changes to existing AI solutions.
Four kits are available for download namely Visual Quality Control, Customer Chatbot, Intelligent Document Indexing and Utility Assett Health.
Visual quality control: Quality control (QC) is essential 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.
Developers are looking to infuse AI into their solutions and the reference kits contribute to that goal. The kits build on and complement Intel’s AI software portfolio of end-to-end tools and framework optimizations. Built on the foundation of the oneAPI open, standards-based, heterogeneous programming model, which delivers performance across multiple types of architectures, these tools help data scientists train models faster and at lower cost by overcoming the limitations of proprietary environments.
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.
Download free on the Intel.com AI Reference Kits website.
