SIENTIAPDE-1253: Refactor training workflow and activities to raise exceptions on failure

This commit refactors the training workflow and associated activities to raise exceptions on failure instead of returning success/failure dictionaries. This allows the Temporal workflow to handle errors more effectively and ensures that the workflow stops when a critical error occurs.

Key changes:

- The train_model workflow is introduced to orchestrate the entire training process, including parameter validation, data download, model training, and model saving.
- The validate_train_params activity is added to validate and convert training parameters.
- The train_model and save_model activities are updated to raise exceptions on failure.
- The ExperimentStatus enum is updated to include a new status for orchestrator validation errors.
- The tests are updated to reflect the new exception-based error handling.
- The activities now return the TrainModelResult directly instead of a dictionary.

tests/utils/models/test_experiment_status.py

@@ -15,8 +15,8 @@ def test_experiment_status_values():
 
 
 def test_experiment_status_count():
-    """Test that enum has exactly 7 status values."""
-    assert len(ExperimentStatus) == 7
+    """Test that enum has exactly 8 status values."""
+    assert len(ExperimentStatus) == 8
 
 
 def test_experiment_status_is_string():
@@ -40,7 +40,7 @@ def test_experiment_status_membership():
 def test_experiment_status_iteration():
     """Test that enum can be iterated."""
     statuses = list(ExperimentStatus)
-    assert len(statuses) == 7
+    assert len(statuses) == 8
     assert ExperimentStatus.MAGE_WAITING_PROC in statuses
     assert ExperimentStatus.TRAINING_SUCCESS in statuses
     assert ExperimentStatus.TRAINING_ERROR in statuses