Building Auditable ML Pipelines: Techniques for Explainability & Tamper Resistance

As machine learning (ML) systems move from research labs into critical production environments healthcare, finance, cybersecurity, and beyond questions of trust, transparency, and accountability have taken center stage. Organizations must now ensure that their models are explainable, traceable, and resistant to tampering.