diff --git a/pyproject.toml b/pyproject.toml
index 60c33bee5..8c5e25a29 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,18 +91,32 @@ disallow_untyped_defs = true
 disallow_incomplete_defs = true
 disallow_untyped_decorators = true
 
+# Probably Unintended Branches/Blocks
+# warn_unreachable = true
+
 # Safety/Upgrading Mypy
 warn_unused_ignores = true
 warn_redundant_casts = true
 warn_unused_configs = true
 show_error_codes = true
 
+# Misc Strictness Settings
+strict_concatenate = false
+strict_equality = true
+
+# Additional Error Codes
+enable_error_code = [
+    # "redundant-expr",
+    # "possibly-undefined",
+    # "unused-awaitable",
+    # "ignore-without-code",
+    # "mutable-override",
+]
+
 [[tool.mypy.overrides]]
 # Ignore packages that are not used or not typed
 module = [
   "coloredlogs",
-  "smartredis",
-  "smartredis.error",
   "redis.cluster",
   "keras",
   "torch",
diff --git a/setup.cfg b/setup.cfg
index 43178d47a..eeac3fbe3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -68,5 +68,7 @@ exclude =
     smartredis
 
 [options.package_data]
+smartsim =
+    py.typed
 smartsim._core.bin =
     *
diff --git a/smartsim/_core/utils/redis.py b/smartsim/_core/utils/redis.py
index 6c592d0f3..7d76aa1bd 100644
--- a/smartsim/_core/utils/redis.py
+++ b/smartsim/_core/utils/redis.py
@@ -177,6 +177,8 @@ def set_ml_model(db_model: DBModel, client: Client) -> None:
                     outputs=db_model.outputs,
                 )
             else:
+                if db_model.model is None:
+                    raise ValueError(f"No model attacted to {db_model.name}")
                 client.set_model(
                     name=db_model.name,
                     model=db_model.model,
@@ -203,7 +205,7 @@ def set_script(db_script: DBScript, client: Client) -> None:
                 client.set_script_from_file(
                     name=db_script.name, file=str(db_script.file), device=device
                 )
-            else:
+            elif db_script.script:
                 if isinstance(db_script.script, str):
                     client.set_script(
                         name=db_script.name, script=db_script.script, device=device
@@ -212,7 +214,8 @@ def set_script(db_script: DBScript, client: Client) -> None:
                     client.set_function(
                         name=db_script.name, function=db_script.script, device=device
                     )
-
+            else:
+                raise ValueError(f"No script or file attached to {db_script.name}")
         except RedisReplyError as error:  # pragma: no cover
             logger.error("Error while setting model on orchestrator.")
             raise error
diff --git a/smartsim/database/orchestrator.py b/smartsim/database/orchestrator.py
index 31bc1be6c..e586bee1c 100644
--- a/smartsim/database/orchestrator.py
+++ b/smartsim/database/orchestrator.py
@@ -573,7 +573,7 @@ def set_max_message_size(self, size: int = 1_073_741_824) -> None:
         """
         self.set_db_conf("proto-max-bulk-len", str(size))
 
-    def set_db_conf(self, key: str, value: t.Union[int, str]) -> None:
+    def set_db_conf(self, key: str, value: str) -> None:
         """Set any valid configuration at runtime without the need
         to restart the database. All configuration parameters
         that are set are immediately loaded by the database and
diff --git a/smartsim/entity/dbobject.py b/smartsim/entity/dbobject.py
index bebedb12c..368864b40 100644
--- a/smartsim/entity/dbobject.py
+++ b/smartsim/entity/dbobject.py
@@ -33,7 +33,10 @@
 __all__ = ["DBObject", "DBModel", "DBScript"]
 
 
-class DBObject:
+_DBObjectFuncT = t.TypeVar("_DBObjectFuncT", str, bytes)
+
+
+class DBObject(t.Generic[_DBObjectFuncT]):
     """Base class for ML objects residing on DB. Should not
     be instantiated.
     """
@@ -41,14 +44,14 @@ class DBObject:
     def __init__(
         self,
         name: str,
-        func: t.Optional[str],
+        func: t.Optional[_DBObjectFuncT],
         file_path: t.Optional[str],
         device: t.Literal["CPU", "GPU"],
         devices_per_node: int,
         first_device: int,
     ) -> None:
         self.name = name
-        self.func = func
+        self.func: t.Optional[_DBObjectFuncT] = func
         self.file: t.Optional[Path] = (
             None  # Need to have this explicitly to check on it
         )
@@ -65,9 +68,7 @@ def devices(self) -> t.List[str]:
 
     @property
     def is_file(self) -> bool:
-        if self.func:
-            return False
-        return True
+        return not self.func
 
     @staticmethod
     def _check_tensor_args(
@@ -153,7 +154,7 @@ def _check_devices(
             raise ValueError(msg)
 
 
-class DBScript(DBObject):
+class DBScript(DBObject[str]):
     def __init__(
         self,
         name: str,
@@ -214,12 +215,12 @@ def __str__(self) -> str:
         return desc_str
 
 
-class DBModel(DBObject):
+class DBModel(DBObject[bytes]):
     def __init__(
         self,
         name: str,
         backend: str,
-        model: t.Optional[str] = None,
+        model: t.Optional[bytes] = None,
         model_file: t.Optional[str] = None,
         device: t.Literal["CPU", "GPU"] = "CPU",
         devices_per_node: int = 1,
@@ -276,7 +277,7 @@ def __init__(
         self.inputs, self.outputs = self._check_tensor_args(inputs, outputs)
 
     @property
-    def model(self) -> t.Union[str, None]:
+    def model(self) -> t.Optional[bytes]:
         return self.func
 
     def __str__(self) -> str:
diff --git a/smartsim/entity/ensemble.py b/smartsim/entity/ensemble.py
index 28ada31de..74bdcfba4 100644
--- a/smartsim/entity/ensemble.py
+++ b/smartsim/entity/ensemble.py
@@ -357,7 +357,7 @@ def add_ml_model(
         self,
         name: str,
         backend: str,
-        model: t.Optional[str] = None,
+        model: t.Optional[bytes] = None,
         model_path: t.Optional[str] = None,
         device: t.Literal["CPU", "GPU"] = "CPU",
         devices_per_node: int = 1,
diff --git a/smartsim/entity/model.py b/smartsim/entity/model.py
index 6b97cbf2e..dcafce464 100644
--- a/smartsim/entity/model.py
+++ b/smartsim/entity/model.py
@@ -455,7 +455,7 @@ def add_ml_model(
         self,
         name: str,
         backend: str,
-        model: t.Optional[str] = None,
+        model: t.Optional[bytes] = None,
         model_path: t.Optional[str] = None,
         device: t.Literal["CPU", "GPU"] = "CPU",
         devices_per_node: int = 1,
diff --git a/smartsim/ml/data.py b/smartsim/ml/data.py
index 3dfca9f0c..4a28c7bb9 100644
--- a/smartsim/ml/data.py
+++ b/smartsim/ml/data.py
@@ -35,6 +35,9 @@
 from ..error import SSInternalError
 from ..log import get_logger
 
+if t.TYPE_CHECKING:
+    import numpy.typing as npt
+
 logger = get_logger(__name__)
 
 
@@ -118,7 +121,7 @@ def download(self, client: Client) -> None:
         if "target_name" in field_names:
             self.target_name = info_ds.get_meta_strings("target_name")[0]
         if "num_classes" in field_names:
-            self.num_classes = info_ds.get_meta_scalars("num_classes")[0]
+            self.num_classes = int(info_ds.get_meta_scalars("num_classes")[0])
 
     def __repr__(self) -> str:
         strings = ["DataInfo object"]
@@ -311,8 +314,8 @@ def __init__(
         self.address = address
         self.cluster = cluster
         self.verbose = verbose
-        self.samples = None
-        self.targets = None
+        self.samples: t.Optional["npt.NDArray[t.Any]"] = None
+        self.targets: t.Optional["npt.NDArray[t.Any]"] = None
         self.num_samples = 0
         self.indices = np.arange(0)
         self.shuffle = shuffle
@@ -460,14 +463,20 @@ def _add_samples(self, indices: t.List[int]) -> None:
         if self.samples is not None:
             for dataset in datasets:
                 self.samples = np.concatenate(
-                    (self.samples, dataset.get_tensor(self.sample_name))
+                    (
+                        t.cast("npt.NDArray[t.Any]", self.samples),
+                        dataset.get_tensor(self.sample_name),
+                    )
                 )
                 if self.need_targets:
                     self.targets = np.concatenate(
-                        (self.targets, dataset.get_tensor(self.target_name))
+                        (
+                            t.cast("npt.NDArray[t.Any]", self.targets),
+                            dataset.get_tensor(self.target_name),
+                        )
                     )
 
-            self.num_samples = self.samples.shape[0]
+            self.num_samples = t.cast("npt.NDArray[t.Any]", self.samples).shape[0]
             self.indices = np.arange(self.num_samples)
 
         self.log(f"New dataset size: {self.num_samples}, batches: {len(self)}")
@@ -496,8 +505,8 @@ def update_data(self) -> None:
             np.random.shuffle(self.indices)
 
     def _data_generation(
-        self, indices: np.ndarray  # type: ignore[type-arg]
-    ) -> t.Tuple[np.ndarray, np.ndarray]:  # type: ignore[type-arg]
+        self, indices: "npt.NDArray[t.Any]"
+    ) -> t.Tuple["npt.NDArray[t.Any]", "npt.NDArray[t.Any]"]:
         # Initialization
         if self.samples is None:
             raise ValueError("Samples have not been initialized")
@@ -505,10 +514,10 @@ def _data_generation(
         xval = self.samples[indices]
 
         if self.need_targets:
-            yval = self.targets[indices]
+            yval = t.cast("npt.NDArray[t.Any]", self.targets)[indices]
         elif self.autoencoding:
             yval = xval
         else:
-            return xval
+            return xval  # type: ignore[no-any-return]
 
         return xval, yval
diff --git a/smartsim/ml/tf/data.py b/smartsim/ml/tf/data.py
index ae0b9aadd..c5e93261c 100644
--- a/smartsim/ml/tf/data.py
+++ b/smartsim/ml/tf/data.py
@@ -31,6 +31,9 @@
 
 from smartsim.ml import DataDownloader
 
+if t.TYPE_CHECKING:
+    import numpy.typing as npt
+
 
 class _TFDataGenerationCommon(DataDownloader, keras.utils.Sequence):
     def __getitem__(
@@ -60,7 +63,9 @@ def on_epoch_end(self) -> None:
         if self.shuffle:
             np.random.shuffle(self.indices)
 
-    def _data_generation(self, indices: np.ndarray) -> t.Tuple[np.ndarray, np.ndarray]:  # type: ignore[type-arg]
+    def _data_generation(
+        self, indices: "npt.NDArray[t.Any]"
+    ) -> t.Tuple["npt.NDArray[t.Any]", "npt.NDArray[t.Any]"]:
         # Initialization
         if self.samples is None:
             raise ValueError("No samples loaded for data generation")
@@ -68,13 +73,13 @@ def _data_generation(self, indices: np.ndarray) -> t.Tuple[np.ndarray, np.ndarra
         xval = self.samples[indices]
 
         if self.need_targets:
-            yval = self.targets[indices]
+            yval = t.cast("npt.NDArray[t.Any]", self.targets)[indices]
             if self.num_classes is not None:
                 yval = keras.utils.to_categorical(yval, num_classes=self.num_classes)
         elif self.autoencoding:
             yval = xval
         else:
-            return xval
+            return xval  # type: ignore[no-any-return]
 
         return xval, yval
 
diff --git a/smartsim/py.typed b/smartsim/py.typed
new file mode 100644
index 000000000..e69de29bb