Environment-Aware Data Governance and Drift Monitoring
Large-Scale Analytics Enterprise
KEY IMPACT
Delivered a cross-environment drift management solution exceeding industry best practices, automated retraining readiness with zero manual intervention, and enhanced compliance reporting for all production model events.
The Challenge
A large-scale analytics environment required automated detection of data drift across production and UAT workspaces, with strict separation and audit control for model retraining.
Our Solution
We built a governed MLOps automation framework in Databricks to detect and respond to drift events across multiple environments. The solution implemented a drift detection engine monitoring model input distributions. Upon threshold breach, a controlled Feature Store refresh pipeline was triggered in the non-production workspace to pre-stage new training data. The deployment pipeline leveraged Databricks CLI v0.25+ and Azure DevOps YAML CI/CD workflows, using service principal authentication for secure workspace-to-workspace communication. All workflows were versioned and auditable via Unity Catalog lineage tracking.
Results & Outcomes
Delivered a cross-environment drift management solution exceeding industry best practices
Automated retraining readiness with zero manual intervention
Enhanced compliance reporting for all production model events
Technologies Used
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