Performance evaluation of an AI model

The service helps customers by evaluating the performance of their AI models with diverse benchmark datasets. Before the service, customers may lack clarity on how their AI models perform in specific real-world applications. After the service, they gain detailed insights into the effectiveness, accuracy, and potential improvement areas of their AI models, enabling them to optimise performance for targeted use cases.

The service will be delivered remotely, leveraging a suite of benchmark datasets tailored to the AI model’s intended use cases. The evaluation process includes multiple iterations to ensure comprehensive coverage of performance metrics. The duration of the service depends on the model's complexity and the volume of testing required, generally taking a few days to weeks. Customers receive a detailed performance report, which includes accuracy metrics, insights into strengths and weaknesses, and recommendations for improvement. Customers must provide their AI models and weights in a standard file format (e.g., .pb, .pkl, .yaml, .onnx, .h5) and any related documentation, including intended use case details.

Customisation options include selecting specific datasets, performance metrics, and testing scenarios aligned with the AI model's applications. Customers may request a focus on certain data types or performance benchmarks relevant to their use case. Limitations include compatibility with the provided datasets. If no benchmark dataset is available, the data can be collected in an additional service.