Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning

This project represents an academic thesis or paper focused on a specific technique in the broader field of AI safety and reliability. While the approach of using training trajectories for uncertainty (similar to Snapshot Ensembles or Stochastic Weight Averaging) is technically sound, it lacks any defensible moat. With 0 stars and minimal forks, there is no evidence of community adoption or ecosystem development. From a competitive standpoint, frontier labs like OpenAI and Anthropic already invest heavily in reliability and calibration (e.g., using RLHF and 'verifiers'). Furthermore, companies like Cleanlab and various Conformal Prediction libraries (like MAPIE) offer more mature, production-ready tools for these tasks. The risk of platform domination is high because cloud providers (AWS SageMaker, Google Vertex AI) are increasingly baking 'reliability' and 'uncertainty' scores directly into their managed training pipelines. This project is likely to remain a reference implementation rather than a standalone product.