predict_real_time

MultiModalCloudPredictor.predict_real_time(test_data: str | DataFrame, test_data_image_column: str | None = None, accept: str = 'application/x-parquet', **kwargs) → Series

Predict with the deployed endpoint. A deployed endpoint is required. This is intended to provide a low latency inference. If you want to inference on a large dataset, use predict() instead.

Parameters:

• test_data (Union(str, pandas.DataFrame)) – The test data to be inferenced. Can be a pandas.DataFrame, or a local path to csv file.

• test_data_image_column (default = None) – If provided a csv file or pandas.DataFrame as the test_data and test_data involves image modality, you must specify the column name corresponding to image paths. The path MUST be an abspath

• accept (str, default = application/x-parquet) – Type of accept output content. Valid options are application/x-parquet, text/csv, application/json

• kwargs – Additional args that you would pass to predict calls of an AutoGluon logic

Returns:

• Pandas.Series

• Predict results in Series