MCML: A Universal Schema for AI Traceability & Lifecycle Governance l PLEYVERSE AI

🎥 From the MLOps World | GenAI Summit 2025 — Virtual Session (October 6, 2025) Session Title: MCML: A Universal Schema for AI Traceability and Lifecycle Governance Speakers: Lanre Ogunkunle, Sr. AI Engineer, & Alex Olaniyan, Project Manager, PLEYVERSE AI Talk Track: Governance, Auditability & Model Risk Management Abstract: The deployment of AI systems in critical domains like healthcare, finance, and autonomous systems has intensified regulatory scrutiny and demands for transparent, auditable AI practices. Current documentation methods—while valuable—often remain fragmented, lacking interoperability and lifecycle coverage. In this session, Lanre Ogunkunle and Alex Olaniyan introduce the Model Connect Markup Language (MCML), a unified schema-based governance framework that integrates model, dataset, interface, and agent documentation into a comprehensive traceability system. They demonstrate how MCML enables end-to-end AI traceability from development through inference, supporting compliance with frameworks such as the EU AI Act, NIST RMF, and FDA SaMD, while facilitating cross-organizational interoperability. Real-world results show a 40% reduction in audit prep time and improved incident response. What you’ll learn: • How to implement lifecycle traceability using MCML • How to map AI artifacts to compliance frameworks (NIST, EU AI Act, FDA) • How to integrate MCML into CI/CD pipelines and MLOps stacks • Best practices from MCML adoption in healthcare, finance, and autonomous systems.