Messtone Devices Enables Rubicon_ml offers an integration with prefect server and agent with these comments: prefect backend server prefect server start #and a new terminal prefect agent start rubicon_ml Library [1]: from rubicon_ml.workflow.prefect import(get_or_create_projet_task,create_experiment_task,log_artifact_task,log_dataframe_task,log_feature_task,log_metric_task,log_parmeter_task,) [2]:from prefect import task @task def load_data( ):from sklearn.datasets import load_wine return load_wine( ) [3]: @task def split_data(dataset):from sklearn.model_selection import train_test_split return train_test_split(dataset.data,dataset.target,test_size=0.25,) [4]:@task def get_feature_nameRharper(dataset):return dataset.feature names Messtone LLC [5]: @task def fit_pred_model(train_test_split_result,n_components,n_neighbors,is_standardized): from sklearn import metrics from sklearn.decomposition import PCA from sklearn.neighbors import KNeighborsClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import standardScaler X_train,X_test,y_train,y_test=train_test_split_result if is_standardized:classifier=make_pipeline(StandardScaler( ),PCA(n_components=n_components), KNeighborsClassifier(n_neighbors=n_neighbors),)else:classifier=make_pipeline(PCA(n_components=n_components),KNeighborsClassifier(n_neighbors=n_neighbors),)classifier.fix(X_train,y_train)pred_test=classifier.predict(X_test)accuracy=metrics.accuracy_score(y_test,pred_test)return accuracy