Updating training

Now Azure Data Factory allows you to do just that with the newly released Azure MLUpdate Resource activity.

With Azure ML you typically first setup your scoring and training experiments, then two separate web service endpoints for each experiment.

In this blog, I have presented an end-to-end scenario for retraining and updating Azure ML web service models.

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The operationalized retraining and updating scenario in ADF consists of the following elements: For step-by-step instructions on setting up the scoring, retraining and update activities for the model complete with JSON examples, check out out our predictive pipelines documentation.Customers working with Azure Machine Learning models have been leveraging the built in Azure MLBatch Execution activity with Azure Data Factory pipelines to operationalize the ML models in production and score new data against the pre-trained models at scale.But as trends and variables that influence the model’s parameters change over time, ideally this pipeline should also support recurring automated retraining and updates to the model with latest training data.A training web service receives training data and produces trained model(s).A scoring web service receives unlabeled data examples and makes predictions.

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