Automating an end-to-end scoring solution

Ultimately, the end goal of any AutoML project is to create an automated scoring solution. Data gets pulled in from a source, scored automatically using the model you trained, and the results get stored in a location of your choice. By combining everything you've learned in the previous three sections, you can accomplish this task easily.

You will begin this section by opening up AMLS, creating a new dataset, and slightly altering your existing Iris-Scoring-Pipeline. Then, after republishing your pipeline with a new name, you will combine it with the Copy data activity you created to load data into Azure.

Next, you will create another Copy Data activity to transfer your results from Azure to your PC and schedule the job to run once a week on Mondays. This is a very common pattern in ML, and it's one you can accomplish without any code at all using ADF.

Editing an ML pipeline to score new data

First, you need to create...