Creating the monitoring model
We create a new model by using the create
method. Where we can define things like how the data should be chunked, the key performance metric, etc.
# Create model
model = nml_sdk.monitoring.Model.create(
name='Example model',
schema=schema,
chunk_period='MONTHLY',
reference_data=reference_data,
analysis_data=analysis_data,
target_data=target_data,
key_performance_metric='F1',
)
More info about the Model
class can be found in its API reference.
In case you are wondering why we need to pass the reference_data
twice —once in the schema and another in the model created— the reason is that both steps are treated differently. In the schema inspection step, we transmit only a few rows (100 to be precise) to the NannyML Cloud server for deriving the schema. While when creating the model, the entire thing is uploaded, so that will take a bit more time.
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