Defining the Model Schema
Model schema and custom metrics for a Machine Learning Model can be defined at the time of logging the model.
client = mlfoundry.get_client()
run = client.create_run(project_name="project_name")
model_version = run.log_model(
name="demo-model",
model=model,
framework="sklearn",
description="model description",
model_schema={
"features": [
{"name": "feature1", "type": "float"},
{"name": "feature2", "type": "string"},
],
"prediction_type": "categorical",
},
custom_metrics=[{"name": "log_loss", "type": "metric", "value_type": "float"}],
)
The schema can also be viewed and edited from the monitoring dashboard by clicking on view schema button:

Viewing and Editing Model Schema
Updated 4 months ago