MLOps & Model Governance

Models built in notebooks rarely survive the journey to production: environments differ, data lineage is undocumented, and deployment depends on the one person who understands the code.

• 1Models trapped in notebooks with environments that cannot be reproduced
• 2Deployment depends on individuals rather than repeatable pipelines
• 3Silent drift degrades model quality with no monitoring or alerting

CloudGate stands up an end-to-end MLOps capability on Azure Machine Learning with versioned data and models, registered environments and CI/CD pipelines in Azure DevOps or GitHub Actions, so every model reaches production the same way.

• Versioned datasets, features, environments and models in the Azure ML registry
• CI/CD pipelines for training, validation, approval and deployment
• Automated data drift, prediction drift and quality monitoring with alerting

Typical outcomes for this solution pattern, not the results of a named client engagement.