The Challenge

A global leader in HVAC technology provides smart appliances to tens of thousands of consumers worldwide. These appliances continuously collect sensor-level 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:

1. A model for energy savings estimation

2. A model for leak detection and health monitoring of devices

While the models were promising, the company faced a clear challenge: how to productionize these models at scale, with the right infrastructure, governance, and monitoring in place.

The Solution

Common Sense AI designed and implemented a robust MLOps infrastructure using Microsoft Azure Machine Learning (AML). We worked closely with the client to set up all necessary components, including:

• Scalable compute environments

• Centralized model registry and deployment pipelines

• Integration with a third-party feature store (Feast)

• Model monitoring and retraining workflows

The architecture followed best practices in security, reproducibility, and scalability, ensuring the system was ready not just for current use cases, but also for future AI initiatives.

The Result

With the new MLOps environment in place, the client was able to:

• Deploy both patented ML models to production

• Reduce time-to-market for future model development

• Monitor and retrain models with ease

• Gain full control over versioning, testing, and endpoint management

The solution now serves as a scalable foundation for delivering AI-enabled value to both consumers and the business, while aligning with enterprise-grade development standards.