The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications – Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 – highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains. After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned. 1 INTRODUCTION In the context of Artificial Intelligence (AI), the evo- lution of computing paradigms from centralized data centers to the edge of the network heralds a transfor- mative shift in how AI applications are developed, deployed, and operated. This transition is critical to realizing the full potential of AI across a wide range of industries, from healthcare to agriculture and industrial maintenance, by leveraging the immedi- acy and context-aware capabilities of edge comput- ing (Sun et al., 2020; Singh and Gill, 2023). Specif- ically, the edge computing paradigm is characterized by processing data directly in the devices where it is collected, such as smartphones, wearables, and IoT. Edge computing significantly reduces latency, con- serves bandwidth, and enhances data privacy and se- curity, thereby catalyzing the realization of real-time, responsive AI solutions.AI-SPRINT is a collective project funded by the European Commission under the Horizon 2020 pro- gram, emerging as a cornerstone in this evolving land- scape. The goal of AI-SPRINT, which stands for Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum, is to provide a comprehen- sive design and runtime framework dedicated to ac- celerating the development and deployment of AI ap- plications across the computing continuum. This con- tinuum spans from cloud data centers to edge de- vices and sensors, integrating AI components to op- erate seamlessly and efficiently. The project’s core objective is to offer a suite of tools that enable an optimal balance among application performance, en- ergy efficiency, and AI model accuracy, all while up- holding rigorous security and privacy standards. This approach addresses pivotal challenges such as flex- ibility, scalability, and interoperability in distributed computing environments. Moreover, AI-SPRINT fa- cilitates the smooth integration of security and pri- vacy constraints from the design phase of AI appli- cations, thus fostering resilience and trustworthiness as development guiding principles. The aim of this work is to provide a comprehensive overview of the AI-SPRINT framework and the results obtained over the years of its development by examining three use cases: Personalized Healthcare, Maintenance and In- spection, and Farming 4.0. These scenarios showcase the practical implications and transformative potential of the AI-SPRINT framework. The Personalized Healthcare use case focuses on harnessing the power of AI and wearable technolo- gies for health monitoring. By integrating quantita- tive data on heart functions from wearable device sen- sors with qualitative lifestyle information, the project aims to develop a personalized stroke risk assess- ments model. This initiative is particularly critical given the prevalence of stroke among the aging pop- ulation, marking it as a significant cause of death and disability globally (Feigin et al., 2022). Leveraging the AI-SPRINT framework, the project enables effi- cient resource distribution and computation across the edge-to-cloud continuum, facilitating real-time, non- invasive and secure monitoring and risk assessment. The second use case of our overview, Maintenance and Inspection, showcases the use of AI models for the early detection and analysis of windmill blade damage through the use of images captured by drones. By addressing challenges such as bandwidth limi- tations, unstable connections, and the drones’ lim- ited operational flight time, this use case underscores the framework’s ability to enhance industrial effi- ciency and operator efficacy by optimizing the man- agement of computational resources across the edge- cloud continuum. Furthermore, the integration of AI- SPRINT tools facilitates a seamless interaction with cloud-based analytics and monitoring. Finally, the Farming 4.0 use case innovates viti- culture by harnessing the power of edge computing and AI to improve the management of phytosanitary vineyard treatments. By addressing the inefficien- cies of conventional pesticide application methods, which often lead to either excessive or insufficient use (European Commission, 2022), this initiative signifi- cantly mitigates environmental impact, health risks, and farmers’ economic losses. The rest of the paper is organized as follows: Sec- tion 2 offers an overview of AI-SPRINT and its tools to furnish context and introduce the foundational el- ements necessary for comprehending the remainder of the manuscript. Section 3 elaborates on the use cases, individually describing them and demonstrat- ing the application of the tools, along with the prin-cipal outcomes achieved throughout the project’s du- ration. Section 4 summarizes the lessons learned and consolidates the findings. Section 5 concludes the ar- ticle. 2 THE LANDSCAPE OF AI-SPRINT TOOLS The goal of the AI-SPRINT project is to create a com- prehensive framework to accelerate the development and deployment of AI applications across the spec- trum of edge cloud computing. This initiative pro- vides developers with tools to balance application per- formance metrics-such as latency, throughput, and en- ergy efficiency-with AI model accuracy, all within a secure and private framework. In particular, the AI-SPRINT suite provides a design environment, called AI-SPRINT Studio, equipped with abstractions for efficient communica- tion and parallelization using specialized resources such as GPUs and AI sensors. This environment al- lows performance and security requirements to be ex- pressed through quality annotations and uses perfor- mance tools, often based on machine learning mod- els, to automate the profiling of AI applications and the training of the most accurate models. It facilitates the deployment of deep networks in diverse com- puting environments and enables developers to train high-quality models with minimal machine learn- ing (ML) expertise through AI Neural Architecture Search (NAS) algorithms. In addition, design space exploration tools help optimize resource efficiency and reduce cloud usage costs for complex applica- tions. On the runtime side, the AI-SPRINT framework supports system operators with tools for seamless de- ployment and execution on heterogeneous architec- tures. Key features include support for continuous deployment, concurrent execution, resilience to node failures, automated edge-based model retraining, and the use of privacy-preserving and federated learning techniques. It also provides optimized scheduling of accelerator devices for better resource management. Packaging tools further simplify deployment by au- tomating the generation of component placement con- figurations and creating application component im- ages for different architectures, facilitating deploy- ment across multiple hardware platforms. Deploy- ment tools streamline resource configuration across the cloud and edge, orchestrating the deployment of AI applications without manual intervention for im- proved resource efficiency and scalability. Security is built into both phases with tools for de- Figure 1: Overview of the main AI-SPRINT tools, divided into Studio and Runtime, and related third-party services. ploying compute instances in Trusted Execution En- vironments, securing communication channels with authentication, encryption and code verification, and using secure boot and OS attestation. The Secure Networks feature leverages 5G device authentica- tion to establish secure tunnels in accordance with pre-defined security policies. Developers can artic- ulate security requirements through code annotations, which are then translated into tangible security mech- anisms through the Security Policy Builder. In the following, we briefly present the main tools developed in the AI-SPRINT project that have been applied to the use cases. The organization of these tools is presented in Figure 1. Our goal is to provide the reader with the necessary insights to understand how these tools have been integrated and used effec- tively, while maintaining a high-level discussion. 2.1 PyCOMPS/dislib The PyCOMPS Distributed Computing Library (Py- COMPS/dislib) is a Python library that coordinates distributed algorithms for mathematical and machine learning tasks with a simple interface. Aimed at Python developers, PyCOMPS/dislib removes the complexity of parallelization, making it easy to implement large-scale machine learning workflows. At its core, it uses distributed arrays for efficient parallel operations. Designed for versatility, Py- COMPS/dislib integrates applications and is compat-ible with various computing environments, includ- ing clouds and supercomputers, ensuring broad ac- cessibility for developing complex machine learning solutions ( ´Alvarez Cid-Fuentes et al., 2019). Py- COMPS/dislib has been used in the Personalized Healthcare use case. 2.2 POPNAS Pareto-Optimal Progressive Neural Architecture Search (POPNAS) is an advanced AutoML algorithm designed to optimize neural network architectures by balancing multiple performance objectives. It uses a progressive strategy to efficiently narrow the search space, focusing on configurations that provide the best trade-off between competing factors such as accuracy and resource usage. By aiming for Pareto optimality, POPNAS effectively navigates the trade- offs in network design and identifies architectures that achieve a harmonious balance of performance metrics. This approach significantly streamlines the architecture discovery process, making it faster and more resource efficient, while ensuring that the final designs satisfy a comprehensive set of constraints. The research strategy has been considered for im- age classification, time series classification, and image segmentation (Lomurno et al., 2021; Falanti et al., 2022, 2023). POPNAS has been used in the Personalized Healthcare and Farming 4.0 use cases. 2.3 SGDE The Secure Generative Data Exchange (SGDE) framework is designed to enhance data sharing in fed- erated environments, such as hospitals and universi- ties, without compromising individual privacy. Un- like traditional federated learning, which relies on the exchange of model parameters, SGDE enables clients to train data generators on their local infrastructure. These generators are then uploaded to a central server, where clients can access and use them offline to cre- ate large, diverse datasets for various machine learn- ing tasks. SGDE offers key advantages over tradi- tional federated learning. First, it provides access to representative data without sharing sensitive informa- tion. Second, it reduces the need for frequent data ex- changes lowering bandwidth requirements. Finally, it improves security against data poisoning attacks by allowing easier anomaly detection in the gener- ated data (Lomurno et al., 2022; Lampis et al., 2023). SGDE has been used in the Personalized Healthcare use case. 2.4 Privacy Preserving Component The Privacy Preserving Component is designed to balance accuracy with resilience to privacy attacks in deep learning models. Specifically, it relies on adver- sarial training to address the dual goals of improving accuracy and reducing susceptibility to membership inference attacks by optimizing model weights. It also conducts membership inference attacks to evalu- ate a model’s defense against such threats. This com- ponent plays a critical role in strengthening the se- curity of models with respect to membership infer- ence attacks on highly sensitive data. It uses AUC metrics to measure the risk of confidential informa- tion leakage during attacks and to refine training pro- cesses. The resulting models demonstrate both high effectiveness and robust defense against membership inference attacks, with test attacks approaching ran- dom guessing levels (Lomurno and Matteucci, 2022; Lomurno et al., 2023). This tool has been used in the Personalized Healthcare use case. 2.5 SPACE4AI-R The SPACE4AI-R tool manages the runtime of AI ap- plication components, focusing on optimal placement and resource selection across the computing contin- uum. It uses a combination of Random Search and Stochastic Local Search algorithms to adapt to run- time workload variations, aiming for cost-effective reconfigurations of initial deployments. This toolguarantees performance across multiple resources, in- cluding edge devices, cloud GPU-based virtual ma- chines, and function-as-a-service solutions. Through real-world experiments, SPACE4AI-R has proven its ability to efficiently reconfigure deployments in sce- narios such as wind turbine blade damage detection, providing significant cost savings over static deploy- ments while maintaining fast execution times. De- veloped in C++ with a Python interface for han- dling scenario-specific input and output, SPACE4AI- R uses performance models from the aMLLibrary. These models require a regressor file for each ap- plication component and device in the computing continuum, underscoring the tool’s adaptability and precision in resource management (Filippini et al., 2023). SPACE4AI-R has been used in the Person- alized Healthcare use case. 2.6 SPACE4AI-D The SPACE4AI-D tool is designed to optimize the deployment of AI applications by performing design space exploration to identify the most efficient re- source allocation across different computational lay- ers. The goal is to minimize execution costs while meeting performance constraints and ensuring opti- mal component and resource assignments. This tool uses performance models to predict execution time for AI inference tasks and incorporates the Solid li- brary’s advanced metaheuristics, such as local search and tabu search, to refine its selection process. This tool has been used in the Personalized Healthcare use case. 2.7 Infrastructure Manager The Infrastructure Manager (IM) is a comprehen- sive service for orchestrating virtual infrastructures and the applications that run on them. It manages the entire lifecycle, from resource provisioning and initial deployment to configuration, reconfiguration, and eventual termination of those resources. To meet user needs, IM can integrate with catalogs such as the Virtual Machine Image Repository and Cata- logue (VMRC) or the EGI AppDB to facilitate the search for the most appropriate virtual machine im- ages (VMIs). To automate configuration tasks, IM uses Ansible, leveraging a collection of roles from Ansible Galaxy. This setup ensures that the orches- tration process is both efficient and tailored to specific user needs, underpinned by a high-level architecture that incorporates external dependencies for enhanced functionality. This tool has been used in the Person- alized Healthcare, Maintenance and Inspection, and Farming 4.0 use cases. 2.8 AI-SPRINT Monitoring Subsystem The AI-SPRINT Monitoring Subsystem (AMS) is in- tegral to managing time series data and logs within complex, hierarchical multi-layer deployments. It en- sures adherence to the AI-SPRINT framework’s con- straints, offering capabilities for data collection, stor- age, forwarding, and analysis. Upon detecting a violation of framework constraints, AMS alerts the SPACE4AI-R REST API endpoint. AMS is crucial for monitoring key performance metrics and allows for the creation of custom metrics suited to specific needs or for consolidating data across multiple com- ponents. It enables easy incorporation of metrics into applications and facilitating the use of Python dec- orators for creating ad-hoc metrics and aggregating data from various sub-components. This tool has been used in the Maintenance and Inspection and Farming 4.0 use cases. 2.9 OSCAR OSCAR is an open source platform designed to en- able Functions as a Service (FaaS) for file processing applications, leveraging the scalability and flexibility of multi-cloud environments. It enables the creation of highly parallel, event-driven serverless applications that run in custom Docker containers on an elastic Ku- bernetes cluster. This cluster dynamically adjusts its size based on the workload, supporting efficient re- source management across different deployments, in- cluding minified Kubernetes distributions such as K3s for continuous computing workflows. The platform triggers processing jobs by uploading files to MinIO, although it also supports other storage systems such as Amazon S3 and OneData. OSCAR’s deployment is streamlined through the Infrastructure Manager, al- lowing for rapid setup and scalability. Remarkably, OSCAR’s architecture is compatible with constrained devices, including Raspberry Pis, due to its support for the arm64 architecture, demonstrating its versa- tility and adaptability to a wide range of computing environments. OSCAR has been used in the Person- alized Healthcare, Maintenance and Inspection, and Farming 4.0 use cases. 2.10 SCONE SCONE is a runtime solution designed to enhance the security of applications by integrating them into Trusted Execution Environments (TEEs) such as In- tel SGX at compile time. It goes beyond simply en-abling TEEs by providing transparent file system en- cryption and securing communications. A critical fea- ture of SCONE is its ability to perform attestation, which ensures that the application runs securely and remains unmodified within a TEE enclave. Success- ful attestation allows SCONE to provide applications with the necessary configurations and guarantees the protection of confidential data and private keys from unauthorised access. Embedded in the executable it- self, SCONE also communicates with the Configura- tion and Attestation Service, which oversees the at- testation process and manages the distribution of se- crets and configurations, strengthening the security infrastructure around applications running in TEEs. SCONE has been used in the Personalized Healthcare use case. 3 USE CASES ANALYSIS AND RESULTS In this section, we explore the practical application of AI-SPRINT use cases, namely Personalized Health- care, Maintenance and Inspection, and Farming 4.0. We thoroughly examine each case, detailing the data collection methods, integration of tools, and the final evaluation stage. 3.1 Personalized Healthcare The Personalized Healthcare use case leverages AI to improve health monitoring, with a particular fo- cus on assessing and preventing stroke risk through the use of wearables and smartphones. This in- novative method combines quantitative cardiac data with qualitative lifestyle insights to develop an AI model that improves resource utilization across edge- to-cloud platforms, ensuring efficiency and data par- allelism. Recognizing the significant impact of stroke in the elderly – a leading cause of death and disabil- ity worldwide – the project uses non-invasive, con- tinuous monitoring techniques to accurately distin- guish atrial fibrillation signals from normal signals. It achieves this through a pilot study that demonstrates how AI can provide valuable insights for stroke care, while maintaining strict compliance with GDPR for data protection. In addition, the initiative uses fed- erated learning to prioritize privacy and security, al- lowing cloud-based models to be updated with local data and synthetic private data to be shared without compromising personal information. Figure 2 depicts an overview of the Personalized Healthcare use case architecture. 3.1.1 Data Collection and Processing The data type foundational to this use case is fully represented by the information that can be acquired from portable or wearable devices equipped with sen- sors. Specifically, despite the variety of vital param- eters that can be monitored in real time, electrocar- diogram (ECG) recordings were selected for use. The work proceeded along two parallel tracks. The first involved the collection of new data and the valida- tion of the collection protocol, while the second fo- cused on analyzing the best methods for processing ECG signal databases already available in the liter- ature, with a sample size already sufficient to enable effective generalization by machine learning and deep learning models. Data collection was carried out by anonymizing all subject information, in full compliance with the GDPR, and resulted in the recording of 15-day con- tinuous ECGs obtained from a pilot study conducted in Spain, involving a total of 26 Spanish volunteers. This dataset is complemented by lifestyle informa- tion collected through a questionnaire designed by AI-SPRINT’s medical consultant at a large hospital in Spain. Co-organized by BSC, a stroke founda- tion and a medical device manufacturer Freno Al Ic- tus (stroke foundation) and Nuubo (device manufac- turer), two monitoring campaigns were conducted in October 2022 and March 2023. From these, a single case of dropout was observed out of the total number of data collection participants, indicating high adher- ence. Among the 25 remaining individuals, 11 of the re- cruited subjects had a past history characterized by strokes of various types, while the remaining 14 were healthy. The age of the participants ranged from 31 to 78 years, with a mean age of 37 years. The gen- der distribution includes 11 females (5 with a history of stroke) and 14 males (6 with a history of stroke). The qualitative questionnaire includes a wide range of health information, facilitating the assessment of car- diovascular risk factors, lifestyle choices, and medical history. All ECG recordings underwent expert evalu- ation, including review by an independent diagnostic center (IDTF) and review by certified cardiac techni- cians (CCT). To develop algorithms to analyze these data, the publicly available ECG dataset provided as part of the PhysioNet/CinC Challenge (Goldberger et al., 2000; Clifford et al., 2017) was used. These data, consist- ing of thousands of single-variate sequences from the atrial fibrillation and normal classes, were processed to handle class imbalance using shuffling-based aug- mentation and addressed varying sequence lengths with zero padding. Next, two different approaches Figure 2: Architecture of the Personalized Healthcare ap- plication led to two types of algorithm analysis. The first, remaining in the time series domain, involves fea- ture extraction using Short-Time Fourier Transform (STFT), followed by dimensionality reduction using Principal Component Analysis, with the goal of us- ing distributed ML models that are fast, lightweight, and benefit from strong parallelization during train- ing. The second approach uses Fast Fourier Trans- form (FFT) to create spectrograms of the sequences, which are transformed to the image domain using spe- cific hyperparameters: NFFT at 128, frequency at 2500 Hz, and overlap at 16. The resulting images are formatted to 64x64 pixels with 3 color channels, preparing for the application of 2D CNNs for image classification. 3.1.2 Tools Integration This use case required the integration of several tools developed within AI-SPRINT. For the implementa- tion and training of machine learning models for the detection of AF, PyCOMPS/dislib was extensively used both for the development of individual mod- els and for the creation of federated learning archi- tectures, providing an additional layer of privacy for all users. The integration of the POPNAS auto ma- chine learning tool with the Privacy Preserving Com- ponent enabled the construction of deep neural net- works specifically optimised for spectrogram image classification, providing a solution that is both highly efficient and privacy-preserving. The SGDE cross- silo federated learning algorithm also demonstrated the potential for scenarios where clinics and hospitals can share synthetic data with each other. This allows individual privacy to be maintained while facilitating knowledge sharing and addressing data imbalances. The SPACE4AI-D and SPACE4AI-R evaluation tools were effectively used to assess metrics such as conver- gence speed, model accuracy and resource allocation. The security measures in place ensure compliance with privacy and confidentiality standards, including GDPR-compliant data collection processes and a pro- cessing environment secured by SCONE. In addi- tion, monitoring tools and log management systems have been integrated to monitor performance, identify anomalies and support troubleshooting efforts, with successful application in the use case during the in- ference phase via IM, AMS and OSCAR. 3.1.3 Evaluation Overall, the Personalized Healthcare use case achieved excellent results both in terms of AI models performance and integration with the ecosystem. In particular, the dataset in time-series format has been exploited with PyCOMPS/dislib to build a 1D-CNN able to achieve 90% of accuracy with a 5-folds cross validation evaluation technique. Additionally, a 2D CNNs has been developed using the Neural Archi- tecture Search algorithm from POPNAS in combina- tion with the Privacy Preserving Component. The re- sult is a privacy-preserving spectrogram classifier for atrial fibrillation achieving an accuracy of 93.5% on the test set while making privacy attacks ineffective – only slightly better than random guessing. This re- sults demonstrate the effectiveness of the alternative 2D time series processing. Naturally, this approach is more computationally intensive than 1D, as it deals with images rather than series, so it can easily be cou- pled with the first, depending on the constraints of the available infrastructure. Concerning privacy, the Privacy Preserving Component has been used to im- prove model resilience against Membership Inference Attacks (MIAs) (Shokri et al., 2017). Specifically, this tool provides model-aware privacy guarantees on top of the infrastructure-level robustness guarantees of the AI-SPRINT ecosystem. This tool allows to test the effectiveness of MIAs carried out by third parties using three different attack models: threshold-based, logistic regression and random forest. Finally, SGDE has been used to generate synthetic ECG data. In fact, by utilizing synthetic data, users gain access to di- verse and extensive datasets while maintaining indi- viduals’ confidentiality. 3.2 Maintenance and Inspection The Maintenance and Inspection use case focuses on developing solutions in the area of infrastructure in- spection through the use of proprietary AI machine learning technology. This system provides automated processes to detect, classify and monitor damage to wind turbines in real time by flying drones, with data seamlessly transferred to edge cloud architectures for comprehensive analysis. Wind turbines typically have a lifespan of 20-25 years, but damage to components such as blades, generators and gearboxes can drasti- cally reduce this lifespan. Repair costs can range from$300,000 for a single blade replacement to $5 million for a complete station overhaul. To mitigate the risk of failure, thorough maintenance and inspection is criti- cal. Currently, most inspection companies are moving toward drone-based asset inspection. The data col- lection process is either manual or semi-automated. Data is manually downloaded from the drone to the laptop, which is then uploaded to the cloud. AI sys- tems play a key role by processing large amounts of data to more accurately predict and analyze main- tenance needs. The AI-SPRINT infrastructure ac- celerates AI-driven inspection processes by reducing operator analysis time and minimizing human error through the integration of machine learning systems, as well as the harmonious integration of cloud-based analysis and localized processing using lightweight data pattern recognition routines. Figure 3 shows an overview of the Maintenance and Inspection use case architecture. 3.2.1 Data Collection and Processing A European branch of a US software firm developed the Maintenance and Inspection solution. As a user of AI-SPRINT, this company leverages its own data from past projects along with its AI machine learn- ing algorithm. The goal is to transition from cloud- based to real-time feedback, shifting data processing to drones (Unmanned Aerial Vehicle, UA Vs) and edge servers. This approach uses existing legacy platform components and models, focusing on operational ef- ficiency rather than the initial design and model train- ing. The standard data processing workflow for wind turbine blade inspection, supported by proprietary software, starts with gathering data. Operators cap- ture images of the blades using drones, either manu- ally or with semi-automated techniques. Next, opera- tors evaluate the quality of these images. The data is then uploaded to a cloud-based tool management sys- tem. When the data becomes available, analysts begin the initial analysis with a proprietary platform to ver- ify data completeness. Following this initial check, analysts conduct a comprehensive review of the re- sults, making any necessary adjustments, before cre- ating a detailed PDF report that outlines their findings. 3.2.2 Tools Integration AI-SPRINT tools enable the harmonious integration of cloud-based analytics and local processing for in- spection and maintenance tasks. By leveraging the framework’s capabilities, organizations can optimize efficiency by offloading complex analysis to the cloud while performing lightweight tasks locally. This ap- proach enables fast and accurate maintenance opera- Figure 3: Architecture of the Maintenance and Inspection application tions, improving equipment reliability and minimiz- ing downtime. Offline capability allows modules (UA V , Edge, Cloud) in the field to operate in inde- pendent clusters. Model updates to edge devices are easily managed, while mission metrics such as flight times and model results are conveniently collected and analyzed by the AMS. Although some UA V ana- lytics modules fall short of the ¡1s response time re- quirement, they still operate within a 2s window be- tween shots given the possibility of fluid UA Vs. Flex- ible deployment mechanisms for ML models across different infrastructures are available, with the capa- bility for significant data processing achieved through OSCAR-based processing, provisioning the virtual- ized computing infrastructure in both private and pub- lic cloud setups using IM. The system efficiently pro- cesses large amounts of data within tight timeframes, meeting the requirement to inspect a single turbine within 1-2 minutes. Using OSCAR-based processing, the infrastructure provides scalability and flexibil- ity between synchronous and asynchronous models. Testing on data from a single turbine inspection, with an average of 1 damage found per 3 images, confirms the system’s capability. When deployed on a cluster of 3 c5.2x large AWS machines, asynchronous tasks completed processing in less than 108 seconds. Cloud computing metrics are efficiently collected and ana- lyzed, with alerting mechanisms supported by AMS and performance annotation tools. While dynamic provisioning and scaling was not tested in the use case for migrating components between the edge and the cloud or scaling up cloud resources when compute needs increase, log collection and browsing is facili- tated by AMS, ensuring consistent collection, central- ization, and analysis of logs from all platform compo- nents.3.2.3 Evaluation The integration of AI-SPRINT in the Maintenance and Inspection use case enhanced operations, en- abling a shift from discrete, sequential, batch-oriented blocks to dynamic, near real-time processing. This facilitated a continuous flow of data, bypassing tra- ditional bottlenecks such as asset management dur- ing transfer. The AI-SPRINT tools provided a robust and flexible environment that was cloud provider ag- nostic, enabling serverless processing, efficient provi- sioning and infrastructure management, and continu- ous monitoring. This paradigm shift streamlined de- velopment and operations, improving system respon- siveness and adaptability across diverse industrial in- spections. The use case evaluation indicates signifi- cant success in the asset inspection process through the implementation of AI-SPRINT tooling. The tests exceeded expectations, processing a significant vol- ume of images ranging from 350 to 400 per inspec- tion, exceeding the target of 300. The response time for quality assessment, while varying between 1.5 and 2.5 seconds, remained sufficient for smooth UA V op- eration and met the KPI of less than 2 seconds, al- beit with slightly less accuracy. The system pro- vided timely feedback on major damage, often within 5 minutes of inspection completion, ensuring action- able insights. It also achieved a 30% reduction in data transmitted to the cloud, with reductions reach- ing 80% for newer turbines and over 35% for older turbines. Overall, the asset inspection process suc- cessfully met or exceeded specified KPIs, demonstrat- ing effectiveness in image processing, response time, feedback delivery, and data transfer reduction. 3.3 Farming 4.0 The agricultural sector is embracing a digital revo- lution with the integration of GPS technologies and smart sensors paving the way for Precision Agricul- ture (PA). Euractiv highlights that nearly 80% of new agricultural equipment now features PA components, representing the collaboration of around 4500 man- ufacturers, across 450 machine types, and involving approximately 135,000 employees worldwide. This digital transformation is projected to unlock a poten- tialC26 billion in value from next-generation agricul- ture in the near future. Within this landscape, the Farming 4.0 use case, powered by the AI-SPRINT framework, sets out to refine phytosanitary treatments through advanced AI and edge computing technologies. The application focuses on the development and deployment of AI models that optimize the use of phytosanitary prod- Figure 4: Architecture of the Farming 4.0 application ucts, ensuring precise product application and mini- mizing environmental impact. By integrating intelli- gent sensors on grape harvesting machines, the use case gathers crucial data for enhancing crop moni- toring and management. From these data, a stream- lined image collection and analysis process has been developed between edge and cloud devices. AI mod- els obtained via neural architecture search guarantee a state-of-the-art performance in disease detection and grape identification. Figure 4 depicts an overview of the Farming 4.0 use case architecture. 3.3.1 Data Collection and Processing The data collection for the Farming 4.0 is used to sup- port the development and verification of an advanced spraying workflow, optimized through smart farming devices. It relies on two distinct phases. The initial phase involves a tractor equipped with smart sensors, including two Intel RealSense depth cameras (D435i), a Stereolabs ZED 2i RGB camera, a Fleet Car-PC with a GTX1050Ti GPU, a GPS Module (Reach M+ UA V RTK kit), and a Crystalfontz CFA-635 display. This setup is used to simulate the final spraying work- flow’s data input requirements as accurately as possi- ble. Over the course of seven weeks in July and Au- gust 2022, this phase yielded a dataset comprising 464 RGB images, accounting for more than 7,000 labeled objects, alongside GPS, time, and acceleration data. This comprehensive data collection facilitated the de- tection of foliage volume and canopy shape, critical for optimizing the phytosanitary treatment workflow. The second phase of data collection occurred in mid-2023, coinciding with the field testing of the fi- nalized spraying workflow in a production environ- ment. To enrich the dataset and ensure its robustness against varying environmental conditions, additional images were captured using mobile devices. This ex- panded dataset was labeled through a combination of automated tools and manual effort, followed by expert review, amounting to 75 images. The datasets gener- ated during this phase are publicly accessible, provid- ing valuable resources for ongoing research and de- velopment in precision agriculture (Skaylink and Gre-goire, 2023a,b). 3.3.2 Tools Integration The AI-SPRINT suite facilitated the development of application code in an infrastructure-agnostic manner, delegating deployment to the OSCAR service. This approach streamlined the packaging of the entire ex- ecution environment onto edge devices, ensuring a seamless integration process. OSCAR node clusters are designed to scale automatically in response to the runtime workload, utilizing Kubernetes for efficient operations. Within the context of the farming applica- tion, classification and segmentation metrics are man- aged and monitored through the use of AI-SPRINT Studio annotations and the AMS. These metrics are stored into InfluxDB and are made accessible through visualization in Grafana. For tasks such as grape disease detection and fruit volume estimation, image files gathered from sensors are automatically uploaded to the cloud, facilitating real-time data processing. The configuration of both application and device settings is streamlined through a unified YAML file, allowing for rapid modifications to be made across the system. Moreover, updates to edge devices are managed using tags, which permits targeted updates that can be specifically tailored based on criteria such as grape varieties and geographical regions, enhancing the precision and efficiency of the deployment process. 3.3.3 Evaluation Without AI-SPRINT, farming services encountered numerous challenges such as cloud-service integra- tion, infrastructure adaptability, secure edge device deployment, and limited access to advanced AI tools. The AI-SPRINT project has profoundly transformed the Farming 4.0 solutions by effectively tacking these issues. Notably, key achievements include the effi- cient deployment of smart farming devices on tractors leveraging edge computation and cloud infrastructure. The system successfully managed performance met- rics collection and data transfer with a processing time of 0.8 seconds per image while handling over five images in parallel. Additionally, data processing has been carried on multiple cloud platforms to show- case the framework flexibility with rapid transition. The best foliage segmentation model was developed using the neural architecture search algorithm from POPNAS, obtaining a mean accuracy of 96.8% and mean intersection-over-union of 94.2%. 4 LESSONS LEARNED AND RECOMMENDATIONS The AI-SPRINT initiative, through its application in Personalized Healthcare, Maintenance and Inspec- tion, and Farming 4.0, has clearly demonstrated the ability of AI to revolutionize various industries by im- proving efficiency, adaptability, and performance. In the Personalized Healthcare use case, the use of AI- SPRINT enabled significant advances in stroke care, where its precision in detecting atrial fibrillation from ECG signals with high accuracy. This success was un- derpinned by the integration of PyCOMPS/dislib for distributed machine learning and the innovative use of federated learning, which together improved predic- tive performance while ensuring data security through encrypted transmissions. The use of OSCAR for on- demand inference and the strategic implementation of quality annotations early in the design process were critical to meeting the specific requirements of the healthcare sector. As for the Maintenance and Inspec- tion use case, the project encountered system orches- tration challenges due to the resource constraints of components such as the Nvidia Jetson Nano. This led to a shift towards the development of a lightweight, dedicated component, highlighting the importance of flexibility and the need to tailor solutions to spe- cific operational contexts. This adaptation under- scores a broader lesson within the AI-SPRINT jour- ney: the need for pragmatic and resource-conscious approaches to technology deployment. In agriculture, the Farming 4.0 use case demonstrated the transfor- mative potential of AI-SPRINT in smart farming de- vices, where it significantly reduced the deployment effort and enhanced the real-time processing capabil- ities of farming equipment. The use of OSCAR and the POPNAS tool for AI model optimization exem- plifies the project’s success in combining rapid data processing with actionable insights for on-the-fly de- cision making, thereby optimizing agricultural output and resource use. Together, these implementations validate AI-SPRINT’s role as a catalyst for innova- tion across industries, providing lessons on the value of distributed machine learning, the need for secure and efficient data processing, and the benefits of adap- tive, context-aware technology solutions. Through its diverse applications, AI-SPRINT not only addresses specific industry challenges, but also sets a precedent for future AI-driven efforts, emphasizing a holistic approach that combines technical excellence with op- erational pragmatism.5 CONCLUSIONS The AI-SPRINT framework has demonstrated its ver- satility and effectiveness in a variety of industry sce- narios, such as Personalized Healthcare, Maintenance and Inspection, and Agriculture 4.0. This adaptabil- ity is underscored by significant achievements such as the development of a GDPR-compliant stroke risk assessment system in healthcare, real-time windmill blade damage detection for maintenance, and im- proved pesticide application efficiency in agriculture, all facilitated by AI-enabled tools. In Personalized Healthcare, the framework demonstrated its ability to not only adhere to strict privacy standards, but also de- liver high processing efficiency, illustrating the power of its applications in critical healthcare areas. In Maintenance and Inspection, there was a notable shift to a near real-time operational model, making devel- opment and operations more seamless with the help of the AI-SPRINT toolkit. In Farming 4.0, the project achieved its goal of increasing the efficiency of pes- ticide spraying by deploying smart farming devices that have yielded positive results. These achievements underscore the significant contributions of the AI- SPRINT project to the field of artificial intelligence, addressing unique challenges across different sectors. The experience gained from these successes, as well as the challenges and lessons learned, provide valu- able insights for future AI developments. This body of work solidifies the AI-SPRINT project’s impact on the ever-evolving artificial intelligence landscape.