Two patented models.
Ready for production.
An Azure MLOps foundation takes the client’s machine learning models from promising prototypes to monitored, maintainable production systems.
A global leader in HVAC technology
The challenge
A global leader in HVAC technology provides smart appliances to tens of thousands of consumers worldwide. These appliances continuously collect sensor data and metadata in real time, generating a rich stream of information across regions and use cases.
The company had developed two patented machine learning models:
- A model that estimates energy savings
- A model for leak detection and health monitoring of devices
The models were promising, but the company faced a clear challenge: how to put them into production at scale, with the right infrastructure, governance and monitoring in place.
The solution
Common Sense AI designed and implemented a robust MLOps infrastructure on Microsoft Azure Machine Learning. Working closely with the client, we set up all the necessary components:
- Scalable compute environments
- A central model registry and deployment pipelines
- Integration with a third-party feature store (Feast)
- Model monitoring and retraining workflows
The architecture follows best practices in security, reproducibility and scalability, so the system is ready for current use cases and for future AI initiatives.
The result
Both patented models now run in production.
- Shorter time to market for future models
- Models are easy to monitor and retrain
- Full control over versioning, testing and endpoint management
The setup is now a scalable foundation for delivering AI to both consumers and the business, in line with enterprise development standards.