Artificial Intelligence (AI), to become fully pervasive, needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is close to where data are produced and therefore crucial to their timely, flexible, and secure management. AI-SPRINT will define a framework for developing AI applications in computing continua, enabling a finely-tuned tradeoff between performance (e.g. in terms of end-to-end latency and throughput) and AI model accuracy, while providing security and privacy guarantees. AI-SPRINT outcomes are: i) simplified programming models to reduce the steep learning curves in the development of AI software in computing continua; ii) highly specialized building blocks for distributed training, privacy preservation and advanced machine learning models, to shorten time-to-market for AI applications; iii) automated deployment and dynamic reconfiguration to decrease the cost of operating AI software. Beneficiaries include end-users of AI systems, software developers, system integrators, and cloud providers. AI-SPRINT tools will make it possible to consider security and privacy early in the design stage and to seamlessly manage the time-varying conditions typical of real environments. Real-world scenarios are an integral part of AI-SPRINT as key to guiding requirements and development and validating results. Three heterogeneous use cases (farming 4.0, maintenance & inspection, and personalized healthcare) are built by industrial partners. Cutting-edge innovation is brought to the Consortium by four research partners with complementary expertise. Two system integrators provide vision on relevant verticals and technology insights, one cloud provider brings real-world implementation expertise, and two specialists in dissemination ensure impacts and uptake. AI-SPRINT will also pursue a sustainability path through the creation of an Alliance and Adopter Acceleration club as a marketplace for AI businesses