flowchart TD subgraph " " pipeline([pipeline.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 pipeline renderMoreInformation "{{pipeline_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}}"