The challenge
The client needed a reliable way to deploy, monitor and evolve a machine learning model that predicts the risk of students dropping out.
Research & Education • Azure AI
MLOps
MLOps for a Student Dropout Prediction Model
Implemented MLOps practices on Azure for a model predicting student dropout, covering versioning, deployment and monitoring.
Ref: ISENA103655
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The approach
Kumulus structured MLOps practices for model versioning, deployment, controlled monitoring and evolution using Microsoft Azure, DevOps and Azure machine learning services.
The outcome
Education teams gained a broader basis for evaluating dropout risk, drawing on academic progress, attendance and disciplinary indicators, and for scaling the approach. The Azure MLOps workflow supports versioning, deployment and monitoring through Azure Machine Learning, Azure DevOps and Azure Monitor.
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