====================================================================== MLflow + Git LFS Model Versioning Playbook (FERC-Compliant) ====================================================================== DEFINITION ---------------------------------------- The MLflow + Git LFS Model Versioning Playbook (FERC-Compliant) is a governance-aligned engineering framework that integrates MLflow’s experiment tracking and model registry with Git LFS for binary artifact storage, enabling auditable, reproducible, and FERC-regulated versioning of forecasting models used in renewable energy dispatch and market participation. It enforces traceability from raw weather/telemetry data through training pipelines to deployed models, satisfying FERC Order No. 888, Order No. 2222, and cybersecurity requirements under NIST SP 800-53 Rev. 5. This playbook standardizes metadata tagging, access controls, retention policies, and immutable audit logs required for regulatory scrutiny of AI-driven grid decisions. OVERVIEW ---------------------------------------- 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. KEY COMPONENTS ---------------------------------------- 1. MLflow Tracking & Model Registry 2. Git LFS with FERC-Aware Storage Policies 3. FERC-Compliant Metadata Schema (FCMS) APPLICATIONS ---------------------------------------- - Versioning day-ahead solar irradiance forecast models for ISO/RTO market submissions - Auditing wind power prediction model updates under FERC Form 731 reporting requirements - Reproducing model behavior during FERC enforcement investigations into forecast-induced imbalance penalties KEY FORMULAS ---------------------------------------- Regulatory Traceability Index (RTI): RTI = (M × L × V) / (T × E) -> Quantifies model version traceability compliance; M = number of mandated metadata fields present, L = lineage depth (number of upstream commits traced), V = version immutability score (1 if tagged + signed, 0 otherwise), T = time since last audit log entry (days), E = number of unverified external dependencies FERC Binary Artifact Retention Score (FBARS): FBARS = 1 − max(0, (D − R) / R) -> Measures adherence to FERC-mandated retention period R (days); D = actual days retained; scores 1.0 indicate full compliance, <0 indicates noncompliance RELATED CONCEPTS ---------------------------------------- - NIST SP 800-53 Rev. 5 (AI Risk Management Framework) - FERC Order No. 2222 (Distributed Energy Resource Aggregation) - ISO/RTO Forecast Submission Standards (e.g., CAISO Forecasting Guidelines) REFERENCES ---------------------------------------- FERC Order No. 2222: Final Rule on Distributed Energy Resource Aggregation (https://www.ferc.gov/news-events/news/ferc-issues-order-2222-final-rule-distributed-energy-resource-aggregation) NIST AI Risk Management Framework (AI RMF) v1.0 (https://www.nist.gov/itl/ai-risk-management-framework) MLflow Model Registry Documentation (v2.14+) (https://mlflow.org/docs/latest/model-registry/index.html) TAGS ---------------------------------------- FERC-compliance, renewable-energy-forecasting, model-versioning, MLflow, Git-LFS, regulatory-AI