This article explores three methods to serve multiple distinct models within a single Azure Machine Learning deployment. It addresses a limitation in AzureML's platform by providing workarounds for managing independently versioned models in one deployment. AI
IMPACT Offers practical solutions for optimizing model serving infrastructure in AzureML.
RANK_REASON The article provides technical workarounds for a specific platform feature, classifying it as a tool-related item.
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