flowchart TD
subgraph " "
recipe([recipe.yaml])
ingestUserCode([steps/ingest.py]) --> ingestMLPStep[["ingest"]]
ingestMLPStep --> dataParquet[(ingested_data)]
ingestScoringUserCode([steps/ingest.py]) --> ingestScoringMLPStep[["ingest_scoring"]]
ingestScoringMLPStep --> dataScoringParquet[(ingested_scoring_data)]
dataScoring[(ingested_scoring_data)] --> predictMLPStep[["predict"]]
predictMLPStep --> dataScored[(scored_data)]
end
data[(ingested_data)] --> splitStep[["split"]]
transformUserCode([steps/transform.py]) --> transformMLPStep
splitUserCode([steps/split.py]) --> splitStep
splitStep --> splitData0[(training_data)]
splitStep --> splitData1[(validation_data)]
splitStep --> splitData2[(test_data)]
splitData0 --> transformMLPStep[["transform"]]
splitData1 --> transformMLPStep[["transform"]]
transformMLPStep --> transformedParquet[(transformed_training_data,
transformed_validation_data)]
transformMLPStep --> transformer{{transformer}}
transformedParquet --> trainMLPStep[["train"]]
trainUserCode([steps/train.py]) --> trainMLPStep
customMetricsUserCode([steps/custom_metrics.py]) --> trainMLPStep
transformer --> trainMLPStep
trainMLPStep --> run{{run}}
trainMLPStep --> model{{model}}
trainMLPStep --> predictedTrainingData[(predicted_training_data)]
model --> evaluateMLPStep[["evaluate"]]
splitData1 --> evaluateMLPStep
splitData2 --> evaluateMLPStep
customMetricsUserCode --> evaluateMLPStep
evaluateMLPStep --> model_validation_status{{model_validation_status}}
run --> registerMLPStep[["register"]]
run --> evaluateMLPStep
model_validation_status --> registerMLPStep
registerMLPStep --> registered_model_version{{registered_model_version}}
click ingestMLPStep renderMoreInformation "{{ingest_step_help}}"
click ingestUserCode renderMoreInformation "{{ingest_user_code_help}}"
click splitStep renderMoreInformation "{{split_step_help}}"
click transformMLPStep renderMoreInformation "{{transform_step_help}}"
click transformUserCode renderMoreInformation "{{transform_user_code_help}}"
click trainMLPStep renderMoreInformation "{{train_step_help}}"
click trainUserCode renderMoreInformation "{{train_user_code_help}}"
click evaluateMLPStep renderMoreInformation "{{evaluate_step_help}}"
click registerMLPStep renderMoreInformation "{{register_step_help}}"
click customMetricsUserCode renderMoreInformation "{{custom_metrics_user_code_help}}"
click splitUserCode renderMoreInformation "{{split_user_code_help}}"
click ingestScoringUserCode renderMoreInformation "{{ingest_user_code_help}}"
click ingestScoringMLPStep renderMoreInformation "{{ingest_scoring_step_help}}"
click predictMLPStep renderMoreInformation "{{predict_step_help}}"
click recipe renderMoreInformation "{{recipe_yaml_help}}"
click dataParquet renderMoreInformation "{{ingested_data_help}}"
click data renderMoreInformation "{{ingested_data_help}}"
click splitData0 renderMoreInformation "{{training_data_help}}"
click splitData1 renderMoreInformation "{{validation_data_help}}"
click splitData2 renderMoreInformation "{{test_data_help}}"
click transformedParquet renderMoreInformation "{{transformed_training_and_validation_data_help}}"
click run renderMoreInformation "{{mlflow_run_help}}"
click model renderMoreInformation "{{fitted_model_help}}"
click predictedTrainingData renderMoreInformation "{{predicted_training_data_help}}"
click transformer renderMoreInformation "{{fitted_transformer_help}}"
click model_validation_status renderMoreInformation "{{model_validation_status_help}}"
click registered_model_version renderMoreInformation "{{registered_model_version_help}}"
click dataScoringParquet renderMoreInformation "{{ingested_scoring_data_help}}"
click dataScoring renderMoreInformation "{{ingested_scoring_data_help}}"
click dataScored renderMoreInformation "{{scored_data_help}}"