diff --git a/src/server/codegen/knowledgepack_codegen_mixin.py b/src/server/codegen/knowledgepack_codegen_mixin.py
index 2043644e..827e10f0 100755
--- a/src/server/codegen/knowledgepack_codegen_mixin.py
+++ b/src/server/codegen/knowledgepack_codegen_mixin.py
@@ -206,20 +206,19 @@ def create_tensorflow_build_flags(self, models):
         for model in models:
             if self.is_tensorflow(model["classifier_config"]["classifier"]):
                 if self.nn_inference_engine == "nnom":
-                    #TODO: NNoM
+                    # TODO: NNoM
                     pass
 
                 if self.nn_inference_engine == "tf_micro":
                     return tf_micro_cflags
 
-
         return ""
 
     def create_kb_model_tf_micro_binary(self, models):
         for model in models:
             if self.is_tensorflow(model["classifier_config"]["classifier"]):
                 if self.nn_inference_engine == "nnom":
-                    #TODO: NNoM
+                    # TODO: NNoM
                     pass
 
                 if self.nn_inference_engine == "tf_micro":
@@ -233,6 +232,9 @@ def create_sml_classification_result_info(self, models_data):
         model_fill["TensorFlow Lite for Microcontrollers"] = (
             "{\n\ttf_micro_model_results_object(kb_models[model_index].classifier_id, (model_results_t *)model_results);\n}"
         )
+        model_fill["Neural Network"] = (
+            "{\n\ttf_micro_model_results_object(kb_models[model_index].classifier_id, (model_results_t *)model_results);\n}"
+        )
 
         model_fill["Decision Tree Ensemble"] = (
             "{\n\ttree_ensemble_model_results_object(kb_models[model_index].classifier_id, (model_results_t *)model_results);\n}"
@@ -256,6 +258,20 @@ def create_sml_classification_result_print_info(self, models_data):
 
             """
 
+        model_fill[
+            "Neural Network"
+        ] = """{
+               sml_classification_result_info(model_index, &model_result);\n
+               pbuf += sprintf(pbuf, ",\\"ModelDebug\\":[");
+               for (int32_t i=0; i<model_result.num_outputs; i++)
+               {
+                 pbuf += sprintf(pbuf, "%f, ", model_result.output_tensor[i]);
+               }
+               pbuf += sprintf(pbuf, "]");
+                }
+
+            """
+
         return self.create_case_fill_template_classifier_type(models_data, model_fill)
 
     def create_classifier_structures(self, classifier_types, kb_models):
@@ -264,16 +280,17 @@ def create_classifier_structures(self, classifier_types, kb_models):
         formated_classifier_types = [
             x.lower().replace(" ", "_") for x in classifier_types
         ]
-        
+
         def is_neural_netwrok(classifier_type):
-            return (classifier_type == "tensorflow_lite_for_microcontrollers" or classifier_type == 'neural_network')
+            return (
+                classifier_type == "tensorflow_lite_for_microcontrollers"
+                or classifier_type == "neural_network"
+            )
 
         formated_classifier_types = [
             x if not is_neural_netwrok(x) else self.nn_inference_engine
             for x in formated_classifier_types
         ]
-        
-        
 
         logger.info(
             {
@@ -313,12 +330,18 @@ def get_classifier_type_map(self, classifier_config):
             return 3
         elif classifier_type in ["Bonsai"]:
             return 4
-        elif classifier_type in ["TF Micro", "TensorFlow Lite for Microcontrollers", "Neural Network"]:
+        elif classifier_type in [
+            "TF Micro",
+            "TensorFlow Lite for Microcontrollers",
+            "Neural Network",
+        ]:
             return 5
         elif classifier_type in ["Linear Regression"]:
             return 6
         else:
-            raise Exception(f"{classifier_type} not supported Classifier Type for code generation")
+            raise Exception(
+                f"{classifier_type} not supported Classifier Type for code generation"
+            )
 
     def create_debug_flagging(self):
         ret = []
@@ -354,7 +377,7 @@ def create_kb_classifier_headers(self, classifier_types):
             output.append('#include "bonsai_trained_models.h"')
 
         if self.is_tensorflow(classifier_types):
-            if self.nn_inference_engine == "nnom":                
+            if self.nn_inference_engine == "nnom":
                 output.append('#include "nnom_trained_models.h"')
                 output.append('#include "nnom_middleware.h"')
 
@@ -385,7 +408,7 @@ def create_kb_classifier_header_calls_only(self, classifier_types):
 
         if self.is_tensorflow(classifier_types):
             if self.nn_inference_engine == "nnom":
-                #TODO: NNoM
+                # TODO: NNoM
                 pass
 
             if self.nn_inference_engine == "tf_micro":
@@ -424,7 +447,7 @@ def create_kb_classifier_init(self, classifier_types):
 
         if self.is_tensorflow(classifier_types):
             if self.nn_inference_engine == "nnom":
-                output.append(c_line(1, "nnom_init(nnom_classifier_rows, 0);"))                
+                output.append(c_line(1, "nnom_init(nnom_classifier_rows, 0);"))
 
             if self.nn_inference_engine == "tf_micro":
                 output.append(c_line(1, "tf_micro_init(tf_micro_classifier_rows, 0);"))
@@ -482,15 +505,15 @@ def create_classifier_calls(self, models_data):
             elif model["classifier_config"]["classifier"] in [
                 "TF Micro",
                 "TensorFlow Lite for Microcontrollers",
-                "Neural Network"
+                "Neural Network",
             ]:
-                if self.nn_inference_engine=='tf_micro':
+                if self.nn_inference_engine == "tf_micro":
                     output_str += c_line(
                         1,
                         "ret = tf_micro_simple_submit(kb_model->classifier_id, kb_model->pfeature_vector, kb_model->pmodel_results);",
                     )
-                if self.nn_inference_engine=='nnom':
-                        output_str += c_line(
+                if self.nn_inference_engine == "nnom":
+                    output_str += c_line(
                         1,
                         "ret = nnom_simple_submit(kb_model->classifier_id, kb_model->pfeature_vector, kb_model->pmodel_results);",
                     )
diff --git a/src/server/codegen/knowledgepack_model_graph_mixin.py b/src/server/codegen/knowledgepack_model_graph_mixin.py
index 43d065f4..faab6e37 100644
--- a/src/server/codegen/knowledgepack_model_graph_mixin.py
+++ b/src/server/codegen/knowledgepack_model_graph_mixin.py
@@ -614,7 +614,7 @@ def get_classifier_init(self, knowledgepack):
         elif classifier_config["classifier"] in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
-            "Neural Network"
+            "Neural Network",
         ]:
             return {"classifier": "Neural Network"}
 
@@ -1618,5 +1618,7 @@ def get_classifier_type(knowledgepack):
 
     if classifier_type == "tensorflow_lite_for_microcontrollers":
         classifier_type = "tf_micro"
+    if classifier_type == "neural_network":
+        classifier_type = "tf_micro"
 
     return classifier_type
diff --git a/src/server/codegen/model_gen/model_gen.py b/src/server/codegen/model_gen/model_gen.py
index e4f5d124..11e95ca2 100644
--- a/src/server/codegen/model_gen/model_gen.py
+++ b/src/server/codegen/model_gen/model_gen.py
@@ -24,7 +24,7 @@
     linear_regression,
     pme,
     tf_micro,
-    nnom
+    nnom,
 )
 from django.core.exceptions import ValidationError
 
@@ -35,12 +35,13 @@
     "bonsai",
     "pme",
     "linear_regression",
-    "nnom"
+    "nnom",
 ]
 
 CLASSIFER_MAP = {
     "decision tree ensemble": "decision_tree_ensemble",
     "tensorflow lite for microcontrollers": "tf_micro",
+    "Neural Network": "tf_micro",
     "nnom": "nnom",
     "pme": "pme",
     "boosted tree ensemble": "boosted_tree_ensemble",
@@ -61,7 +62,7 @@ def get_classifier_type(model_configuration):
     return classifier_type.lower()
 
 
-#TODO: Make this an interface that returns the object instead of having all of these if statements
+# TODO: Make this an interface that returns the object instead of having all of these if statements
 class ModelGen:
     @staticmethod
     def create_classifier_structures(classifier_type, kb_models):
@@ -82,10 +83,10 @@ def create_classifier_structures(classifier_type, kb_models):
 
         if classifier_type == "linear_regression":
             return linear_regression.create_classifier_structures(kb_models)
-        
+
         if classifier_type == "nnom":
             return nnom.create_classifier_structures(kb_models)
-        
+
         return ""
 
     @staticmethod
@@ -107,7 +108,7 @@ def create_max_tmp_parameters(classifier_type, kb_models):
 
         if classifier_type == "linear_regression":
             return linear_regression.create_max_tmp_parameters(kb_models)
-        
+
         if classifier_type == "nnom":
             return nnom.create_max_tmp_parameters(kb_models)
 
@@ -151,7 +152,7 @@ def validate_model_parameters(model_parameters, model_configuration):
 
         if classifier_type == "linear_regression":
             return linear_regression.validate_model_parameters(model_parameters)
-        
+
         if classifier_type == "nnom":
             return nnom.validate_model_parameters(model_parameters)
 
@@ -180,8 +181,7 @@ def validate_model_configuration(model_configuration):
 
         if classifier_type == "linear_regression":
             return linear_regression.validate_model_configuration(model_configuration)
-        
-    
+
         if classifier_type == "nnom":
             return nnom.validate_model_configuration(model_configuration)
 
@@ -204,7 +204,7 @@ def get_output_tensor_size(classifier_type, model):
 
         if classifier_type == "linear_regression":
             return linear_regression.get_output_tensor_size(model)
-        
+
         if classifier_type == "nnom":
             return nnom.get_output_tensor_size(model)
 
@@ -232,7 +232,7 @@ def get_input_feature_type(model):
 
         if classifier_type == "linear_regression":
             return FLOAT
-        
+
         if classifier_type == "nnom":
             return UINT8_T
 
@@ -263,7 +263,7 @@ def get_input_feature_def(model):
 
         if classifier_type == "nnom":
             return UINT8_T
-        
+
         raise ValueError("No classifier type found")
 
     @staticmethod
@@ -273,7 +273,7 @@ def get_model_type(model):
         CLASSIFICATION = 1
         if classifier_type == "tf_micro":
             return CLASSIFICATION
-        
+
         if classifier_type == "nnom":
             return CLASSIFICATION
 
diff --git a/src/server/codegen/model_gen/nnom.py b/src/server/codegen/model_gen/nnom.py
index b2eca32b..2c8aee8e 100644
--- a/src/server/codegen/model_gen/nnom.py
+++ b/src/server/codegen/model_gen/nnom.py
@@ -23,7 +23,7 @@
 
 
 def create_classifier_arrays(model_index, model):
-   
+
     pass
 
 
@@ -32,16 +32,16 @@ def create_classifier_struct(model_index, model):
 
 
 def create_classifier_structures(models):
-    """ typedef struct nnom_classifier_rows
-        {
-            uint16_t num_inputs;
-            uint8_t num_outputs;
-            float threshold;
-            uint8_t estimator_type;
-            nnom_model_t* model;
-        } nnom_classifier_rows_t;
+    """typedef struct nnom_classifier_rows
+    {
+        uint16_t num_inputs;
+        uint8_t num_outputs;
+        float threshold;
+        uint8_t estimator_type;
+        nnom_model_t* model;
+    } nnom_classifier_rows_t;
     """
-    
+
     outputs = []
 
     iterations = 0
@@ -49,11 +49,11 @@ def create_classifier_structures(models):
         if model["classifier_config"].get("classifier", "PME") in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
-            "Neural Network"
+            "Neural Network",
         ]:
-            #outputs.extend(
+            # outputs.extend(
             #    create_tf_micro_classifier_arrays(iterations, model["model_arrays"])
-            #)
+            # )
             iterations += 1
 
     iterations = 0
@@ -61,13 +61,11 @@ def create_classifier_structures(models):
     outputs.append(
         (
             "nnom_classifier_rows_t nnom_classifier_rows[{}] = ".format(
-                utils.get_number_classifiers(
-                    models, "TF Micro"
-                )+utils.get_number_classifiers(
+                utils.get_number_classifiers(models, "TF Micro")
+                + utils.get_number_classifiers(
                     models, "TensorFlow Lite for Microcontrollers"
-                )+utils.get_number_classifiers(
-                    models, "Neural Network"
                 )
+                + utils.get_number_classifiers(models, "Neural Network")
             )
             + "{"
         )
@@ -77,7 +75,7 @@ def create_classifier_structures(models):
         if model["classifier_config"].get("classifier", "PME") in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
-             "Neural Network"
+            "Neural Network",
         ]:
             outputs.append("\n\t{")
             outputs.append(
@@ -111,13 +109,14 @@ def create_classifier_structures(models):
     return outputs
 
 
-
 def create_max_tmp_parameters(kb_models):
-   return []
+    return []
+
 
 def validate_model_parameters(data):
     pass
 
+
 def validate_model_configuration(data):
     return data
 
diff --git a/src/server/codegen/model_gen/tf_micro.py b/src/server/codegen/model_gen/tf_micro.py
index a55b9614..58611a45 100644
--- a/src/server/codegen/model_gen/tf_micro.py
+++ b/src/server/codegen/model_gen/tf_micro.py
@@ -88,7 +88,7 @@ def create_classifier_structures(models):
         if model["classifier_config"].get("classifier", "PME") in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
-            "Neural Network"
+            "Neural Network",
         ]:
             outputs.extend(
                 create_tf_micro_classifier_arrays(iterations, model["model_arrays"])
@@ -112,6 +112,7 @@ def create_classifier_structures(models):
         if model["classifier_config"].get("classifier", "PME") in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
+            "Neural Network",
         ]:
             outputs.append("\n\t{")
             outputs.append(
diff --git a/src/server/engine/recognitionengine.py b/src/server/engine/recognitionengine.py
index 2876a54a..cec187a8 100644
--- a/src/server/engine/recognitionengine.py
+++ b/src/server/engine/recognitionengine.py
@@ -136,6 +136,7 @@ def initialize_classifier(self, config):
         elif self.classifier_type in [
             "TF Micro",
             "TensorFlow Lite for Microcontrollers",
+            "Neural Network",
         ]:
             self.classifier = TensorFlowMicro()
             self.classifier.load_model(self.neuron_array)
@@ -435,7 +436,7 @@ def reco_kb_pipeline(self, kb_description):
             "target_compiler": CompilerDescription.objects.get(
                 uuid="62aabe7e-4f5d-4167-a786-072e4a8dc158"
             ),
-            "nn_inference_engine":'nnom'
+            "nn_inference_engine": "nnom",
         }
 
         logger.userlog(
diff --git a/src/server/library/classifiers/classifiers.py b/src/server/library/classifiers/classifiers.py
index f8d20aba..d1a7a4b6 100644
--- a/src/server/library/classifiers/classifiers.py
+++ b/src/server/library/classifiers/classifiers.py
@@ -39,6 +39,7 @@ def get_classifier(config, save_model_parameters=True):
         "Boosted Tree Ensemble": BoostedTreeEnsemble,
         "TF Micro": TensorFlowMicro,
         "TensorFlow Lite for Microcontrollers": TensorFlowMicro,
+        "Neural Network": TensorFlowMicro,
         "Linear Regression": LinearRegression,
     }
     try:
diff --git a/src/server/library/core_functions/mg_contracts.py b/src/server/library/core_functions/mg_contracts.py
index 56d516a6..fd93d579 100644
--- a/src/server/library/core_functions/mg_contracts.py
+++ b/src/server/library/core_functions/mg_contracts.py
@@ -1274,6 +1274,23 @@ def tensorflow_micro():
 tensorflow_micro_contracts = {"input_contract": [], "output_contract": []}
 
 
+def neural_network():
+    """
+    The Neural Network uses Tensorflow Lite for Microcontrollers, an inference engine
+    from Google optimized run machine learning models on embedded devices.
+
+    Tensorflow Lite for Microcontrollers supports a subset of all Tensorflow functions. For a full
+    list see `all_ops_resolver.cc <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/all_ops_resolver.cc>`_.
+
+    For additional documentation on Tensorflow Lite for Microcontrollers see `here <https://www.tensorflow.org/lite/microcontrollers>`_.
+    """
+
+    return None
+
+
+neural_network_contracts = {"input_contract": [], "output_contract": []}
+
+
 def load_model_tensorflow_micro(
     input_data,
     label_column,
@@ -1433,7 +1450,9 @@ def load_model_tensorflow_micro(
         "type": "list",
         "element_type": "str",
         "handle_by_set": True,
-        "options": [{"name": "TensorFlow Lite for Microcontrollers"}],
+        "options": [
+            {"name": "TensorFlow Lite for Microcontrollers", "name": "Neural Network"}
+        ],
     },
     {"name": "class_map", "type": "dict", "handle_by_set": True, "default": None},
     {
diff --git a/src/server/library/fixtures/functions_prod.yml b/src/server/library/fixtures/functions_prod.yml
index 5f5bfb04..6bbc3900 100644
--- a/src/server/library/fixtures/functions_prod.yml
+++ b/src/server/library/fixtures/functions_prod.yml
@@ -831,6 +831,23 @@
     automl_available: False
   model: library.transform
 
+- fields:
+    core: True
+    name: Neural Network
+    version: 1
+    type: Classifier
+    subtype: NN
+    path: core_functions/mg_contracts.py
+    function_in_file: neural_network
+    has_c_version: False
+    c_file_name:
+    c_function_name:
+    deprecated: False
+    dcl_executable: False
+    uuid: 3994d48f-7b8a-4807-b919-284bbe045928
+    automl_available: False
+  model: library.transform
+
 - fields:
     core: True
     name: Load Model TensorFlow Lite for Microcontrollers
@@ -848,6 +865,22 @@
     automl_available: False
   model: library.transform
 
+- fields:
+    core: True
+    name: Load Model Neural Network
+    version: 1
+    type: Training Algorithm
+    subtype: Load
+    path: core_functions/mg_contracts.py
+    function_in_file: load_model_tensorflow_micro
+    has_c_version: False
+    c_file_name:
+    c_function_name:
+    deprecated: False
+    dcl_executable: False
+    automl_available: False
+  model: library.transform
+
 - fields:
     core: True
     name: Bonsai Tree Optimizer
diff --git a/src/server/library/fixtures/parameter_inventory.yml b/src/server/library/fixtures/parameter_inventory.yml
index 435b3c5d..01967cfe 100644
--- a/src/server/library/fixtures/parameter_inventory.yml
+++ b/src/server/library/fixtures/parameter_inventory.yml
@@ -322,3 +322,16 @@
     binary_classifiers: 0
     allow_unknown: 1
   model: library.parameterinventory
+
+- fields:
+    uuid: e040c660-45c2-4e48-92fc-2a22310076c9
+    function: 3994d48f-7b8a-4807-b919-284bbe045928
+    function_name: Neural Network
+    pipeline_key: classifiers
+    variable_name: null
+    variable_type: null
+    variable_values: null
+    classifiers_optimizers_group: 4
+    binary_classifiers: 0
+    allow_unknown: 1
+  model: library.parameterinventory
diff --git a/src/server/library/fixtures/pipeline_schema.json b/src/server/library/fixtures/pipeline_schema.json
index 7cbab420..5f59f9bc 100755
--- a/src/server/library/fixtures/pipeline_schema.json
+++ b/src/server/library/fixtures/pipeline_schema.json
@@ -312,7 +312,8 @@
         "TransformList": null,
         "Set": false,
         "Exclude": [
-            "TF Micro"
+            "TF Micro",
+            "TensorFlow Lite for Microcontrollers"
         ],
         "Limit": 1
     },
diff --git a/src/ui/src/components/ModelSummary/ModelSummary.jsx b/src/ui/src/components/ModelSummary/ModelSummary.jsx
index 58a58248..4b562399 100644
--- a/src/ui/src/components/ModelSummary/ModelSummary.jsx
+++ b/src/ui/src/components/ModelSummary/ModelSummary.jsx
@@ -22,6 +22,7 @@ import React, { useState, useEffect } from "react";
 import { Box, Typography } from "@mui/material";
 import StandardTable from "components/StandardTable";
 import { ColumnType } from "components/StandardTable/StandardTableConstants";
+import { NN_CLASSIFIER_NAME_LIST } from "consts";
 import NeuralNetworkSummary from "./NeuralNetworkSummary";
 import PMEModelSummary from "./PMEModelSummary";
 
@@ -72,7 +73,7 @@ const ModelSummary = ({ model, showTitle }) => {
           m.pipeline_summary[pipelineSummaryCount - 1].optimizers[0].inputs.max_depth;
       } else if (m.device_configuration?.classifier === "Boosted Tree Ensemble") {
         //
-      } else if (m.device_configuration?.classifier === "TensorFlow Lite for Microcontrollers") {
+      } else if (NN_CLASSIFIER_NAME_LIST.includes(m.device_configuration?.classifier)) {
         //
       }
     }
@@ -131,7 +132,7 @@ const ModelSummary = ({ model, showTitle }) => {
       {model?.data?.device_configuration?.classifier === "PME" && modelParameters ? (
         <PMEModelSummary model={modelParameters} />
       ) : null}
-      {model?.data?.device_configuration?.classifier === "TensorFlow Lite for Microcontrollers" ? (
+      {NN_CLASSIFIER_NAME_LIST.includes(model?.data?.device_configuration?.classifier) ? (
         <NeuralNetworkSummary model={model} />
       ) : null}
     </Box>
diff --git a/src/ui/src/components/PipelineCreateForm/PipelineCreateForm.jsx b/src/ui/src/components/PipelineCreateForm/PipelineCreateForm.jsx
index 9e670b25..c75178e5 100644
--- a/src/ui/src/components/PipelineCreateForm/PipelineCreateForm.jsx
+++ b/src/ui/src/components/PipelineCreateForm/PipelineCreateForm.jsx
@@ -20,6 +20,7 @@ License along with SensiML Piccolo AI. If not, see <https://www.gnu.org/licenses
 import React, { useState } from "react";
 import { Box, Stepper, Step, StepLabel } from "@mui/material";
 import { useTranslation } from "react-i18next";
+import { DEFAULT_CLASSIFIER } from "consts";
 
 import PipelineQueryCreateForm from "components/PipelineQueryCreateForm";
 
@@ -75,7 +76,11 @@ const PipelineCreateForm = ({
         ))}
       </Stepper>
       {activeStep === STEP_PARAMS ? (
-        <PipelineCreateFormParameters classifiers={classifiers} onSubmit={handleSubmitParams} />
+        <PipelineCreateFormParameters
+          classifiers={classifiers}
+          defaultClassifier={DEFAULT_CLASSIFIER}
+          onSubmit={handleSubmitParams}
+        />
       ) : (
         <PipelineQueryCreateForm
           pipelineError={pipelineError}
diff --git a/src/ui/src/consts/CLASSIFIERS.js b/src/ui/src/consts/CLASSIFIERS.js
new file mode 100644
index 00000000..8a289965
--- /dev/null
+++ b/src/ui/src/consts/CLASSIFIERS.js
@@ -0,0 +1,3 @@
+export const NN_CLASSIFIER_NAME_LIST = ["TensorFlow Lite for Microcontrollers", "Neural Network"];
+export const DISABLED_CLASSIFIERS = ["TF Micro", "TensorFlow Lite for Microcontrollers"];
+export const DEFAULT_CLASSIFIER = "Neural Network";
diff --git a/src/ui/src/consts/index.js b/src/ui/src/consts/index.js
index cafb8114..a3fa8e3c 100644
--- a/src/ui/src/consts/index.js
+++ b/src/ui/src/consts/index.js
@@ -24,6 +24,7 @@ import CONSOLE from "./CONSOLE";
 import RESPONSIVE from "./RESPONSIVE";
 import PIPELINE_CONSTS from "./PIPELINE_CONSTS";
 
+export * from "./CLASSIFIERS";
 export * from "./CHARTS_TYPES";
 
 export {
diff --git a/src/ui/src/containers/DownloadModel/components/TargetDeviceOptions.jsx b/src/ui/src/containers/DownloadModel/components/TargetDeviceOptions.jsx
index 1c6f7a6e..9e006f35 100644
--- a/src/ui/src/containers/DownloadModel/components/TargetDeviceOptions.jsx
+++ b/src/ui/src/containers/DownloadModel/components/TargetDeviceOptions.jsx
@@ -41,6 +41,7 @@ import Autocomplete from "@mui/material/Autocomplete";
 import FormCurtain from "components/UIFormCurtain";
 import CheckBoxSelectCard from "components/FormElements/CheckBoxSelectCard";
 
+import { NN_CLASSIFIER_NAME_LIST } from "consts";
 import {
   PLATROFM_TYPES,
   STEP_SELECT_PLATFORM,
@@ -56,8 +57,6 @@ import useStyles from "../DownloadModelStyles";
 import AdvanceSettings from "./AdvanceSettings";
 import defaultSelectedPlatform from "./defaultSelectedPlatform";
 
-const TF_CLASSIFIER_NAME = "TensorFlow Lite for Microcontrollers";
-
 /*
 const defaultEmpty = {
   target_platform: "",
@@ -180,7 +179,7 @@ const TargetDeviceOptions = forwardRef(
         ),
         target_compiler: platform?.default_selections?.compiler || "",
       };
-      if (platform?.nn_inference_engines && classifierName === TF_CLASSIFIER_NAME) {
+      if (platform?.nn_inference_engines && NN_CLASSIFIER_NAME_LIST.includes(classifierName)) {
         const nnList = _.keys(platform.nn_inference_engines);
         if (!_.isEmpty(nnList)) {
           // eslint-disable-next-line prefer-destructuring
diff --git a/src/ui/src/store/autoML/const.js b/src/ui/src/store/autoML/const.js
index 0d817933..aebd9d9c 100644
--- a/src/ui/src/store/autoML/const.js
+++ b/src/ui/src/store/autoML/const.js
@@ -87,7 +87,7 @@ export const TVO_LIST = [
   PIPELINE_STEP_TYPES.VALIDATION,
 ];
 
-export const DISABLED_CLASSIFIERS = ["TF Micro"];
+export const DISABLED_CLASSIFIERS = ["TF Micro", "TensorFlow Lite for Microcontrollers"];
 
 export const ONETIME_ADDED = [FEATURE_GENERATOR, FEATURE_SELECTOR];
 
diff --git a/src/ui/src/store/pipelineTemplates/data/KWSpottingData.json b/src/ui/src/store/pipelineTemplates/data/KWSpottingData.json
index 8abef12a..849c8875 100644
--- a/src/ui/src/store/pipelineTemplates/data/KWSpottingData.json
+++ b/src/ui/src/store/pipelineTemplates/data/KWSpottingData.json
@@ -123,7 +123,7 @@
           ],
           "classifiers": [
             {
-              "name": "TensorFlow Lite for Microcontrollers",
+              "name": "Neural Network",
               "inputs": {}
             }
           ],
@@ -296,7 +296,7 @@
           ],
           "classifiers": [
             {
-              "name": "TensorFlow Lite for Microcontrollers",
+              "name": "Neural Network",
               "inputs": {}
             }
           ],
@@ -501,7 +501,7 @@
           ],
           "classifiers": [
             {
-              "name": "TensorFlow Lite for Microcontrollers",
+              "name": "Neural Network",
               "inputs": {}
             }
           ],
@@ -659,7 +659,7 @@
           ],
           "classifiers": [
             {
-              "name": "TensorFlow Lite for Microcontrollers",
+              "name": "Neural Network",
               "inputs": {}
             }
           ],
diff --git a/src/ui/src/store/pipelineTemplates/data/audioData.json b/src/ui/src/store/pipelineTemplates/data/audioData.json
index 3c5f0336..6be93280 100644
--- a/src/ui/src/store/pipelineTemplates/data/audioData.json
+++ b/src/ui/src/store/pipelineTemplates/data/audioData.json
@@ -168,7 +168,7 @@
           ],
           "classifiers": [
             {
-              "name": "TensorFlow Lite for Microcontrollers",
+              "name": "Neural Network",
               "inputs": {}
             }
           ],
diff --git a/src/ui/src/store/transforms/selectors.js b/src/ui/src/store/transforms/selectors.js
index 82966e42..aa863f27 100644
--- a/src/ui/src/store/transforms/selectors.js
+++ b/src/ui/src/store/transforms/selectors.js
@@ -17,7 +17,7 @@ You should have received a copy of the GNU Affero General Public
 License along with SensiML Piccolo AI. If not, see <https://www.gnu.org/licenses/>.
 */
 
-import { DISABLED_CLASSIFIERS } from "store/autoML/const";
+import { DISABLED_CLASSIFIERS } from "consts";
 
 export const selectTransformsByTypeSubType =
   (type, subtype, transformList, excludeTransform) => (state) => {