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diff --git a/code/sunlab/sunflow/models/utilities.py b/code/sunlab/sunflow/models/utilities.py
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+# Higher-level functions
+
+from sunlab.common.distribution.adversarial_distribution import AdversarialDistribution
+from sunlab.common.scaler.adversarial_scaler import AdversarialScaler
+from sunlab.common.data.utilities import import_dataset
+from .adversarial_autoencoder import AdversarialAutoencoder
+
+
+def create_aae(
+    dataset_file_name,
+    model_directory,
+    normalization_scaler: AdversarialScaler,
+    distribution: AdversarialDistribution or None,
+    magnification=10,
+    latent_size=2,
+):
+    """# Create Adversarial Autoencoder
+
+    - dataset_file_name: str = Path to the dataset file
+    - model_directory: str = Path to save the model in
+    - normalization_scaler: AdversarialScaler = Data normalization Scaler Model
+    - distribution: AdversarialDistribution = Distribution for the Adversary
+    - magnification: int = The Magnification of the Dataset"""
+    dataset = import_dataset(dataset_file_name, magnification)
+    model = AdversarialAutoencoder(
+        model_directory, distribution, normalization_scaler
+    ).init(dataset.dataset, latent_size=latent_size)
+    return model
+
+
+def create_aae_and_dataset(
+    dataset_file_name,
+    model_directory,
+    normalization_scaler: AdversarialScaler,
+    distribution: AdversarialDistribution or None,
+    magnification=10,
+    batch_size=1024,
+    shuffle=True,
+    val_split=0.1,
+    latent_size=2,
+):
+    """# Create Adversarial Autoencoder and Load the Dataset
+
+    - dataset_file_name: str = Path to the dataset file
+    - model_directory: str = Path to save the model in
+    - normalization_scaler: AdversarialScaler = Data normalization Scaler Model
+    - distribution: AdversarialDistribution = Distribution for the Adversary
+    - magnification: int = The Magnification of the Dataset"""
+    model = create_aae(
+        dataset_file_name,
+        model_directory,
+        normalization_scaler,
+        distribution,
+        magnification=magnification,
+        latent_size=latent_size,
+    )
+    dataset = import_dataset(
+        dataset_file_name,
+        magnification,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        val_split=val_split,
+        scaler=model.scaler,
+    )
+    return model, dataset
+
+
+def load_aae(model_directory, normalization_scaler: AdversarialScaler):
+    """# Load Adversarial Autoencoder
+
+    - model_directory: str = Path to save the model in
+    - normalization_scaler: AdversarialScaler = Data normalization Scaler Model
+    """
+    return AdversarialAutoencoder(model_directory, None, normalization_scaler).load()
+
+
+def load_aae_and_dataset(
+    dataset_file_name,
+    model_directory,
+    normalization_scaler: AdversarialScaler,
+    magnification=10,
+):
+    """# Load Adversarial Autoencoder
+
+    - dataset_file_name: str = Path to the dataset file
+    - model_directory: str = Path to save the model in
+    - normalization_scaler: AdversarialScaler = Data normalization Scaler Model
+    - magnification: int = The Magnification of the Dataset"""
+    model = load_aae(model_directory, normalization_scaler)
+    dataset = import_dataset(
+        dataset_file_name, magnification=magnification, scaler=model.scaler
+    )
+    return model, dataset