Model Builder makes it easy to get started with Machine Learning and create your first model. As you gather more data over time, you may want to continuously refine or retrain your model. Using a combination of CLI and Azure tooling, you can train a new ML.NET model and integrate the training into a pipeline. This blog post shows an example of a training pipeline that can be easily rerun using Azure. We’re going to use Azure Machine Learning Datasets to track data and an Azure ML Pipeline to train a new model. This retraining pipeline can then be triggered by Azure DevOps. In this post, we will cover: Creating an Azure Machine Learning Dataset Training a ML.NET model via the Azure Machine Learning CLI (v2) Creating a pipeline in Azure DevOps for re-training Prerequisites Azure Machine Learning Workspace Compute Cluster in the workspace Creating an Azure Machine Learning Dataset Open the workspace in the Microsoft Azure Machine Learning Studio . We need to create a f...
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