Element / Retail - Robust Model Deployment with Azure
Robust Model Deployment with Azure
Problem :
Deployment of machine learning models in production often incurs high infrastructure costs and maintenance overhead. Scalability and performance monitoring needed to be streamlined.
Solution :
The model was deployed via Azure using BlueConic data exports to Azure Blob Storage. The scoring pipeline ran on a B2ms compute engine, with a monthly infrastructure budget of approximately $100. Model retraining was automated through scheduled compute cycles.
Outcome :
The lead scoring system ran with minimal manual intervention, supported by scalable and cost-efficient cloud infrastructure. Total deployment effort was ~120 hours, with clear traceability on performance and updates.