Exercise – deploying a scikit-learn model pipeline with Vertex AI
In this exercise, we will simulate creating a pipeline for an ML model. There will be two pipelines – one to train the ML model and another to predict new data using the model from the first pipeline. We will continue using the credit card default dataset. The two pipelines will look like this:
Figure 8.21 – The steps in the two pipelines
Later in this section, we will load data from BigQuery. But instead of storing the data in pandas, we will write the output to a GCS bucket. We will be doing this as we don’t want to return an in-memory Python object from the function. What I mean by an in-memory Python object, in this case, is a pandas DataFrame. This also applies to other data structures, such as arrays or lists. Remember that every step in Vertex AI Pipelines will be executed in a different container – that is, in a different machine. You can’...
