This article introduces a framework designed to test whether AI model outputs remain consistent when different optimization techniques are applied, such as quantization, pruning, or compiler optimizations. The framework generates test cases, runs inference under various configurations, and compares outputs to detect non-deterministic behavior. This is crucial for production systems where reproducibility is required for debugging, auditing, or compliance. The approach is systematic and includes strategies for handling floating-point variations and hardware differences. It fills a notable gap in current ML deployment practices, where determinism is often assumed but rarely verified.
A novel framework for testing output consistency of AI models across different optimization levels, addressing a critical reliability challenge in ML deployment.