Now we're ready to start work on our pipeline. Pipelines consists of one or more steps which can be Python scripts or they can be specialized steps like an AutoML training estimator or a data transfer step that copies data from one location to another. Each step can run in its own compute context.

So let's proceed to build a simple pipeline that contains an estimator step. A step to train a model and a Python script step that register the train model. And we start by creating a folder to contain the scripts for each step. So after creating our experiment folder, we'll start creating the script that will be used for my estimator.

So we put the necessary libraries. We then set up agPASS which allows us to get parameters passed on to a script from my estimator. Next we get the experiment run context. We then load the diabetes data. We separate features and labels. And then we split data into training and then the test set.

We then train a decision tree model. We calculate the accuracy and log the details. We also calculate the area of the curve and log the details. We save the model and we complete the run. The script for the second step of the pipeline will load the model from where it was saved and then register it in the workspace. It includes a single model folder parameter that contains a path to the folder where the model was saved by the previous step.

We import the necessary libraries, agPASS ,joblib and then we need a workspace class, our model and run. We use agPASS to be able to get the parameters. We get the experiment run context. We load our previously saved model and then registered it in our workspace and complete the run