SIENTIAPDE-1241: Refactor: Improve documentation, exception handling, and configuration in model manager. This commit enhances clarity and robustness by adding detailed docstrings to methods, standardizing exception handling with custom types, and simplifying MLflow configuration.

model_manager/sientia/model_serving.py

@@ -66,8 +66,8 @@ class ModelServing:
         Returns:
             pandas.DataFrame: A DataFrame containing run information.
 
-        Raise:
-            SientiaMlException if unable to search runs
+        Raises:
+            SientiaMlException: If unable to search runs.
         """
         try:
             runs = mlflow.search_runs(experiment_names=experiment_names, order_by=order_by)
@@ -82,6 +82,9 @@ class ModelServing:
 
         Args:
             experiment_identifier (str): name or id of the experiment to be setted
+
+        Raises:
+            Exception: If setting the experiment fails.
         """
         mlflow.set_experiment(experiment_identifier)
 
@@ -99,6 +102,9 @@ class ModelServing:
         Security Warning:
             The GitHub token is hardcoded. Consider moving to environment variable
             or using a secure secret management solution (e.g., K8s secrets).
+
+        Raises:
+            Exception: If logging the model fails.
         """
         mlflow.sklearn.log_model(
             sk_model,
@@ -117,6 +123,9 @@ class ModelServing:
 
         Returns:
             None
+
+        Raises:
+            Exception: If logging the parameter fails.
         """
         mlflow.log_param(key, value)
 
@@ -130,6 +139,9 @@ class ModelServing:
 
         Returns:
             None
+
+        Raises:
+            Exception: If logging the metric fails.
         """
         mlflow.log_metric(key, value)
 
@@ -146,6 +158,9 @@ class ModelServing:
 
         Returns:
             None
+
+        Raises:
+            Exception: If logging the artifact fails.
         """
         mlflow.log_artifact(local_path=local_path, artifact_path=artifact_path, run_id=run_id)
 
@@ -178,11 +193,8 @@ class ModelServing:
         Yields:
             ActiveRun: object that acts as a context manager wrapping the run's state.
 
-        Example:
-            with model_serving.save_experiment(run_name="my_run") as run:
-                model_serving.log_param("param1", value1)
-                model_serving.log_metric("metric1", value2)
-                # Run is automatically closed here, even if an exception occurs
+        Raises:
+            Exception: If starting or ending the MLflow run fails.
         """
         run = mlflow.start_run(
             run_id=run_id,