التحدي
An industrial operator's forecasting models and industrial data assets needed to move into a governed MLOps environment without disrupting near-real-time operational forecasting that was running on an approximately 8-second cycle.
ما الذي قمنا به
Designed a phased lakehouse migration for representative forecasting models: current-state discovery, PI and MQTT data integration, model validation, a Model Serving inference architecture, Unity Catalog Model Registry, MLflow lifecycle management, Databricks Asset Bundles, serverless GPUs, observability, and a path to on-platform retraining, with PyTorch and Weights & Biases patterns considered.
النتيجة
Moved operational forecasting from an approximately 8-second batch cycle to real-time predictions, while strengthening governance, auditability and MLOps resilience for industrial operations.
Moved operational forecasting from an 8-second cycle to real-time predictions, with a governed migration path that improves model lifecycle control.
Phased cutover preserved operational continuity while shifting scoring onto governed Model Serving, with registry promotion, Delta-backed feature and data contracts, and retraining hooks on-platform.