Batch vs Real-Time Scoring in Azure: Key Differences Explained As Artificial Intelligence (AI) and Machine Learning (ML) models become increasingly integral to business operations, deploying these models effectively becomes critical. In Azure, two common approaches to scoring (or inferencing) ML models are Batch Scoring and Real-Time Scoring . Understanding the differences between them is essential to choosing the right solution based on your specific use case, performance requirements, and cost considerations. Batch vs Real-Time Scoring in Azure: Key Differences Explained What is Model Scoring? Model scoring, also known as inference, refers to using a trained machine learning model to make predictions on new data. Once a model is trained and evaluated, it’s deployed to score data and deliver actionable insights. In Azure, this can be achieved through services like Azure Machine Learning , Azure Synapse , and Azure Functions , depending on the scoring approach. Azure AI...
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