MLflow + Git LFS Model Versioning Playbook (FERC-Compliant)

This playbook addresses the unique regulatory and operational constraints of AI-augmented renewable forecasting infrastructure—where model behavior directly impacts wholesale electricity markets, grid reliability, and compliance with Federal Energy Regulatory Commission (FERC) mandates. At its core, it leverages MLflow to capture lineage-rich metadata (e.g., input dataset hashes, hyperparameters, evaluation metrics, and pipeline commit IDs), while delegating large model binaries (e.g., serialized XGBoost ensembles, PyTorch LSTM weights, or ONNX artifacts) to Git LFS, ensuring versioned, deduplicated, and access-controlled storage within enterprise Git repositories. Crucially, the playbook introduces FERC-specific guardrails: mandatory provenance annotations (e.g., 'FERC-Model-Class: Category-II-Aggregate-Forecast'), time-bound retention policies aligned with FERC 60-day recordkeeping rules, and role-based access control (RBAC) mappings compliant with NERC CIP-005 and CIP-010 standards. It further prescribes integration patterns with FERC-mandated audit systems—such as exporting MLflow runs as ISO/IEC 19941-compliant digital evidence packages—and defines validation workflows (e.g., pre-deployment bias testing against historical curtailment events) to satisfy FERC’s fairness and transparency expectations for automated dispatch recommendations.