Model Deployment and
Consump on
Once a predictive model has been developed and trained,
the following steps involve evaluating its performance,
deploying it for application use, and setting up ongoing
monitoring and management mechanisms.
Model Evalua on
• Understanding Model Performance: Evaluating a model's performance
involves using various metrics to understand how well it predicts
outcomes. Key metrics include the ROC curve, which helps assess true
positive rate against false positive rate; the precision-recall curve, which
is vital for understanding the trade-off between precision and recall; and
the confusion matrix, which provides insight into errors the model may
make.
• Tools for Evaluation: Azure Machine Learning Studio offers built-in
modules for generating these evaluation metrics, allowing for visual
assessment of model performance. This ability helps refine the model
further, if necessary, or validate its readiness for deployment.
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