Question: Problem: Following a quicksart guide in official docs (https://mlflow.org/docs/latest/tracking/autolog.html), every time I try running mlflow ui --port 8080 inside parent directory with particular conda env activated same error keeps popping up: C:\Users\user\miniconda3\envs\regular\Lib\site-packages\pydantic\_internal\_config.py:322: UserWarning: Valid config keys have changed in V2: * 'schema_extra' has been renamed to 'json_schema_extra' warnings.warn(message, UserWarning) Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "C:\Users\user\miniconda3\envs\regular\Scripts\mlflow.exe\__main__.py", line 4, in <module> File "C:\Users\user\miniconda3\envs\regular\Lib\site-packages\mlflow\cli.py", line 356, in <module> type=click.Choice([e.name for e in importlib.metadata.entry_points().get("mlflow.app", [])]), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'EntryPoints' object has no attribute 'get'  I have tried multiple versions, reinstalling importlib-metadata (following same issue 'EntryPoints' object has no attribute 'get' - Digital ocean), installing older versions prior to mlflow=2.10.0 as well as older versions of pydantic prior to pydantic==2.6.1 (following the advice of @BenWilson2 from DataBricks on MLflow GH issues). Would really appreciate if anyone could help me wrap my head around the issue. Thanks in advance. Project structure:  regModel.py: import warnings import argparse import logging import pandas as pd import numpy as np import mlflow from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from sklearn.model_selection import train_test_split from sklearn.linear_model import ElasticNet logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) #evaluation function def eval_metrics(actual, pred): rmse = np.sqrt(mean_squared_error(actual, pred)) mae = mean_absolute_error(actual, pred) r2 = r2_score(actual, pred) return rmse, mae, r2 def run_experiment(alpha, l1_ratio): warnings.filterwarnings("ignore") # Read the wine-quality csv file from local data = pd.read_csv("data/red-wine-quality.csv") data.to_csv("data/red-wine-quality.csv", index=False) # Split the data into training and test sets. (0.75, 0.25) split. train, test = train_test_split(data) # The predicted column is "quality" which is a scalar from [3, 9] train_x = train.drop(["quality"], axis=1) test_x = test.drop(["quality"], axis=1) train_y = train[["quality"]] test_y = test[["quality"]] lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) lr.fit(train_x, train_y) predicted_qualities = lr.predict(test_x) (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) print("Elasticnet model (alpha={:f}, l1_ratio={:f}):".format(alpha, l1_ratio)) print(" RMSE: %s" % rmse) print(" MAE: %s" % mae) print(" R2: %s" % r2) mlflow.log_param("alpha", alpha) mlflow.log_param("l1", l1_ratio) mlflow.log_metric("rmse", rmse) mlflow.log_metric("r2", r2) mlflow.log_metric("mae", mae) mlflow.sklearn.log_model(lr, "models")  inroduction.ipynb: 

Answer: Obviously the error was in cli.py. However the appropriate fix was needed, which, thanks to Gabomfim, has been discovered:  just substitute .get("mlflow.app", []) by .select(group="mlflow.app")