introduces a probabilistic testing framework for machine learning pipelines. Unlike deterministic unit tests (Pass/Fail), BTI ML-2 utilizes Bayesian inference to calculate the probability of model degradation or regression. This allows the system to stop running expensive test suites early if confidence is high, or trigger deep validation on specific "Test Items" (data slices) where regression probability is high.