Governing Hundreds of Predictive Models in a Cloud Environment
A large-scale manufacturing operation required continuous process monitoring across hundreds of parallel systems, each requiring dedicated predictive models. The challenge was the governance, resource management, and secure deployment of this massive portfolio in a central cloud environment, ensuring cost optimization. The risk was model collision, resource sprawl, and an unmanageable governance bottleneck.
As a key leader in the team, I was responsible for architecting and managing the cloud-native MLOps infrastructure for the entire model portfolio. This involved establishing standards for deployment, managing massive data storage, and ensuring a single, accountable framework for model lineage and drift maintenance at the enterprise level.
Cloud Architecture & Resource Management: Designed the cloud structure to efficiently manage hundreds of models and petabytes of process data, implementing resource tagging and cost optimization strategies across storage and compute (e.g., using specific managed services).
Standardized Drift Maintenance: Implemented standardized monitoring and automated drift maintenance protocols deployed consistently across the entire model fleet, ensuring uniform reliability and reducing maintenance toil.
Full Lifecycle Management: Managed every phase from development sandbox to production inference and model retirement, emphasizing security and auditable lineage (version control and logging).