Show HN: Resilient RAP: A self-healing data pipeline with <20ms BERT inference (github.com)

A new production-grade framework called Resilient RAP has been announced, designed to provide self-healing capabilities for Reproducible Analytical Pipelines (RAPs) with an impressive BERT inference time of under 20 milliseconds. This innovative tool addresses the significant challenge of schema drift in high-velocity data streams, such as sports telemetry and clinical data, through advanced features like semantic reconciliation and tamper-evident audit trails. The framework autonomously detects changes in data schemas using BERT embeddings for real-time mapping, ensuring that evolving data can be handled efficiently without disrupting analytics processes. The framework's significance lies in its comprehensive approach to data governance and reproducibility, incorporating features like built-in human-in-the-loop validation and structured logging for regulatory compliance. By employing techniques such as SHA-256 for integrity tracking and deterministic pipeline execution, Resilient RAP ensures that users can maintain full provenance and reconstruct analytical results accurately. With pre-built connectors to various domains, including F1 telemetry and NHL play-by-play, this framework not only streamlines data ingestion but also enhances the reliability and auditability of the data analytics process, making it a vital tool for researchers and practitioners in the AI/ML community.