Enhancing AI Data Management: Combining Reservoir Sampling and Self-Adaptive Testing for Efficiency

As data continues to grow exponentially, the processing and generation of foundational data required for Artificial Intelligence (AI) models becomes an expensive task. This paper introduces a method to minimize the dataset required for these models by combining a custom Reservoir Sampling algorithm with an automated, independent, self-adaptive comprehensive data test module. This approach produces a minimized dataset that maintains model accuracy while ensuring essential coverage of business scenarios with the help of a comprehensive data test suite. This method also ensures the integrity and representative-ness of the data required for business use cases and promises a dataset reduction, with an estimated optimization of around 80%. This approach serves as a key resource for organizations aiming to enhance their data handling strategies, ensuring both model accuracy and the comprehensiveness of the data required for AI applications.