Batch Logging - Designed for sending batches of data to Arize
Overview
The Client$log() is designed for training, validation or production environment where batches of data are processed. These environments may be either a R Studio Notebook or a R server that is batch processing lots of backend data.
Import and initialize Arize R client from the Arize Client$new() to call Client$log() with a R data.frame() containing inference data.
Example 1: Logging Features, Predictions, & Actuals
model_id <-"click_through_rate_categorical_vignette_R"# This is the model name that will show up in Arizemodel_version <-"v1.0"# Version of model - can be any stringschema <-create_schema( prediction_id_column_name ="id", feature_column_names = features, prediction_label_column_name ="predictions", prediction_score_column_name ="CTR_predicted", actual_label_column_name ="actuals", actual_score_column_name ="CTR", timestamp_column_name ="model_date")arize_client$log( .data_frame = df_train, .model_id = model_id, .model_version = model_version, .model_type = model_types$SCORE_CATEGORICAL, .environment = environments$TRAINING, .schema = schema)