mlflow

import mlflow import pandas as pd from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from mlflow.models import infer_signature # Set MLflow tracking URI (optional, defaults to local ./mlruns) # For a local server, run 'mlflow ui' in your terminal and point to http://127.0.0.1:5000 # os.environ['MLFLOW_TRACKING_URI'] = os.environ.get('MLFLOW_TRACKING_URI', 'http://127.0.0.1:5000') mlflow.set_experiment("MLflow_Quickstart_Experiment") # Load the Iris dataset X, y = datasets.load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define model hyperparameters params = {"solver": "lbfgs", "max_iter": 1000, "multi_class": "auto", "random_state": 8888} with mlflow.start_run(): # Log hyperparameters mlflow.log_params(params) # Train the model lr = LogisticRegression(**params) lr.fit(X_train, y_train) # Make predictions and calculate metrics y_pred = lr.predict(X_test) accuracy = accuracy_score(y_test, y_pred) mlflow.log_metric("accuracy", accuracy) # Infer model signature predictions = lr.predict(X_train) # Use training data for signature inference signature = infer_signature(X_train, predictions) # Log the model mlflow.sklearn.log_model( sk_model=lr, artifact_path="logistic_regression_model", signature=signature, registered_model_name="IrisLogisticRegression" ) print(f"Logged model with accuracy: {accuracy}") print(f"View runs in MLflow UI: run 'mlflow ui' in your terminal and navigate to http://127.0.0.1:5000")