This is part seven in a series on low-code machine learning with Azure ML.

What Did We Learn?

Over the course of this series, we learned how to use Azure Machine Learning to train models and score data without writing a line of code. We first learned how to create a workspace in Azure Machine Learning. Once we had a workspace in place, we saw how to create datasets and compute resources. Then, we turned Automated Machine Learning (AutoML) loose on our data and saw that, with this dataset, it performed quite well. After that, we tried our own hand at training a model. Then, we made that model available for real-time scoring. Finally, we made the model available for batch scoring.

At this point, we are ready to kick it up a notch and get beyond the basics in Azure Machine Learning. Stay tuned for that!


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