feat: enhance training workflow with model metadata loading and refactor data handling

- Introduced a new activity to load model metadata from the model store.
- Refactored training logic to utilize new model metadata and improved parameter handling.
- Updated the `TrainModelParams` class to include additional fields for model configuration.
- Replaced deprecated utility functions with a custom train-test split implementation.
- Removed unused utility functions and cleaned up the data manager repository.
- Adjusted experiment tracking to include model-specific metadata in notifications.

model_manager/sientia/utils.py

@@ -1,41 +0,0 @@
-from typing import Any
-
-from numpy.typing import ArrayLike
-from sklearn.model_selection import train_test_split
-
-
-def split_train_test(
-    *data: Any,
-    test_size: float | None = None,
-    train_size: float | None = None,
-    random_state: int | None = None,
-    shuffle: bool = True,
-    stratify: ArrayLike | None = None,
-) -> tuple[Any, Any, Any, Any]:
-    """
-    Split arrays or matrices into random train and test subsets.
-
-    Wrapper for sklearn.model_selection.train_test_split.
-
-    Args:
-        *data: data to be split.
-        test_size: size of test subset.
-        train_size: size of train subset.
-        random_state: Seed applied to the data before applying the split.
-        shuffle: Whether or not to shuffle the data before splitting.
-        stratify: If not None, data is split in a stratified fashion, using this as the class labels.
-
-    Returns:
-        X_train, X_test, y_train, y_test
-
-    Thread-safe: This function is stateless and thread-safe.
-    """
-    X_train, X_test, y_train, y_test = train_test_split(
-        *data,
-        test_size=test_size,
-        train_size=train_size,
-        random_state=random_state,
-        shuffle=shuffle,
-        stratify=stratify,
-    )
-    return X_train, X_test, y_train, y_test