components model_evaluation_pipeline

Model Evaluation Pipeline

model_evaluation_pipeline

Overview

Description: Pipeline component for model evaluation for supported tasks. \ Generates predictions on a given model, followed by computing model performance metrics to score the model quality for supported tasks.

Version: 0.0.10

Tags

type : evaluation sub_type : subgraph Preview

View in Studio: https://ml.azure.com/registries/azureml/components/model_evaluation_pipeline/version/0.0.10

Inputs

Name	Description	Type	Default	Optional	Enum
compute_name		string	serverless
instance_type		string	STANDARD_NC24S_V3

model prediction

Name	Description	Type	Default	Optional	Enum
task	Task type	string	tabular-classification		['tabular-classification', 'tabular-classification-multilabel', 'tabular-regression', 'text-classification', 'text-classification-multilabel', 'text-named-entity-recognition', 'text-summarization', 'question-answering', 'text-translation', 'text-generation', 'fill-mask', 'image-classification', 'image-classification-multilabel']
test_data	Test Data	uri_folder		False
mlflow_model	Mlflow Model (could be a registered model or part of another pipeline	mlflow_model		False
label_column_name	Label column name in provided test dataset (Ex: label)	string
input_column_names	Input column names in provided test dataset (Ex : column1). Add comma delimited values in case of multiple input columns (Ex : column1,column2)	string		True
model_uri	[DEPRECATED] The location, in URI format, of the MLflow model, for example: - runs:/<mlflow_run_id>/run-relative/path/to/model - models:/<model_name>/<model_version> - models:/<model_name>/<stage>	string		True
device		string	auto	False	['auto', 'cpu', 'gpu']
batch_size		integer		True

compute metrics

Name	Description	Type	Default	Optional	Enum
evaluation_config	Additional parameters required for evaluation. See How to create a config here	uri_file		True
evaluation_config_params	JSON Serielized string of evaluation_config	string		True

Outputs

Name	Description	Type
evaluation_result	Output dir to save the evaluation result	uri_folder