Solution Cross-industry
MLOps & Model Governance
Industrialize machine learning: reproducible training, automated deployment, monitoring and drift detection.
Days→hoursModel-to-production time
MonitoredDrift and quality alerting
ReproducibleGoverned, auditable lifecycle
Typical outcomes for this kind of engagement. Results depend on your data and starting point.
THE CHALLENGE
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.
- Models trapped in notebooks with environments that cannot be reproduced
- Deployment depends on individuals rather than repeatable pipelines
- Silent drift degrades model quality with no monitoring or alerting
OUR APPROACH
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
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