. You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service? Create an inference pipeline from the training pipelineAdd an Evaluate Model module to the training pipelineClone the training pipeline with a different name
Question
. You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service? Create an inference pipeline from the training pipelineAdd an Evaluate Model module to the training pipelineClone the training pipeline with a different name
Solution
Before deploying the model as a service, you must perform the following steps:
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Create an inference pipeline from the training pipeline: This involves creating a separate pipeline that is specifically designed for making predictions using the trained model. This inference pipeline will take in new data and apply the trained model to generate predictions.
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Add an Evaluate Model module to the training pipeline: This step is important for assessing the performance of the trained model. By adding an Evaluate Model module, you can evaluate the accuracy and other metrics of the model using a validation dataset.
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Clone the training pipeline with a different name: It is recommended to create a copy of the training pipeline with a different name before deploying the model. This allows you to have a separate pipeline for training and another for inference, ensuring that any changes made to the inference pipeline do not affect the training process.
By following these steps, you can ensure that your classification model is properly prepared for deployment as a service.
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