Vertex AI offers a very interesting Model Registry that allows you to store all trained models and track all their versions.
However, I don't manage to create new versions of the same model using the Python SDK. In particular, I have a Vertex AI Pipeline that performs: 1) data preprocessing, 2) feature engineering, 3) feature store creation, and in the end, 4) train a model with AutoML Tabular.
The code of the Pipeline component dedicated to the point 4 is:
automl_training_electric_op = gcc_aip.AutoMLTabularTrainingJobRunOp(
project=project_bq,
model_display_name="pred-model",
display_name="pred-model",
optimization_prediction_type="classification",
optimization_objective="maximize-au-prc",
budget_milli_node_hours=1000,
dataset=comp5a.outputs["dataset"],
target_column="fault",
location=location
)
In the Google documentation I didn't find anything that could help me in creating new versions of the "pred-model", in fact, any time I run the pipeline, Vertex AI creates a new model with the same name.
I would like that at each training, AutoML creates a new version of the same model. E.g., v1, v2, v3.
Here, the current situation, in which the same model is replicated and not versioned:
