Intel, in collaboration with Accenture, has introduced a set of 34 open source AI reference kits that aim to facilitate faster and easier deployment of artificial intelligence (AI) for developers and data scientists.
Each kit consists of model code, training data, machine learning pipeline instructions, libraries, and oneAPI components to optimize AI applications across various environments, including on-premises, cloud, and edge.
These reference kits are built on the oneAPI open, standards-based, heterogeneous programming model and leverage components from Intel’s comprehensive AI software portfolio, such as the Intel AI Analytics Toolkit and the Intel Distribution of OpenVINO toolkit. The objective is to streamline the integration of AI into existing applications, enhance intelligent solutions, and accelerate deployment, providing performance improvements and increasing productivity compared to traditional model development workflows.
The AI reference kits cater to industries like health and life sciences, financial services, manufacturing, retail, and more. Some notable benefits across these sectors include up to 45 percent faster inferencing for an enterprise conversational AI chatbot, up to 20 percent faster training and 55 percent faster inferencing for visual defect detection in life sciences, and a 25 percent increase in prediction accuracy for utility asset health prediction.
By using the oneAPI-powered AI tools and optimizations in these reference kits, developers can experience a reduced time to solution from weeks to days, allowing for faster model training and lower costs, while ensuring portability for open accelerated computing applications.
John Giubileo, managing director at Accenture, commended the collaboration with Intel, stating that the AI reference kits built on oneAPI offer developers a portable and efficient solution for AI projects, reducing complexity and deployment time across various industries.
These reference kits are set to empower millions of developers and data scientists, fostering an AI-driven future by making AI accessible and practical across different sectors.
