Performance Testing of AI Algorithms using HPC

The customer (AI developer or researcher) will receive: Access to a hosted environment to deploy their algorithm and run systematic, automated performance evaluations. Standardised benchmarking reports detailing model accuracy, inference speed, and resource consumption against standardised agrifood reference datasets. Comparative analysis across model versions or alternative architectures to identify the best-performing configuration. Stress-test results covering scalability, robustness to out-of-distribution data, and edge-to-cloud performance. With this, the service solves: The technical evidence gap: developers need objective, reproducible technical metrics to support commercialisation, publication, or regulatory compliance. Compute bottlenecks for high-resolution data: processing drone or hyperspectral imagery requires heavy compute power, which is seamlessly handled by the AgrospAI cluster.

Access is granted via a user-friendly interface or API where developers can upload models as containers alongside standardised test datasets. The execution and stress-testing are handled automatically by the AgrifoodTEF/AgrospAI cluster. Support is provided for containerisation, API integration, and interpreting benchmarking results.

We can customise the benchmarking metrics to evaluate specific parameters (e.g., edge-to-cloud performance simulation, hyperparameter stress testing) and configure dedicated data rooms for private, large-scale algorithmic evaluations. Custom reference datasets from the AgrospAI ecosystem can be incorporated upon agreement with data providers.