diff --git a/training/training/core/celery/worker.py b/training/training/core/celery/worker.py
index fddf5788..071112b3 100644
--- a/training/training/core/celery/worker.py
+++ b/training/training/core/celery/worker.py
@@ -38,7 +38,7 @@ def saveDetailedTrainResultsDataToS3(
     ).put(Body=detailedTrainResultsData.json())
 
 
-def collectClassificationTrainingResults(trainer, basic_info):
+def collectTrainingResults(trainer, basic_info, is_classification):
     trainTestLoss = [
         {
             "x_name": "Epoch",
@@ -58,33 +58,37 @@ def collectClassificationTrainingResults(trainer, basic_info):
         trainTestLoss[0]["y_values"].append(epoch_result.train_loss)
         trainTestLoss[1]["x_values"].append(epoch_result.epoch_num)
         trainTestLoss[1]["y_values"].append(epoch_result.test_loss)
-    confusionMatrix = trainer.generate_confusion_matrix()
-    aucRocCurve = trainer.generate_AUC_ROC_CURVE()
 
-    detailedTrainResultsData = DetailedTrainResultsData(
-        **{
-            "basic_info": basic_info,
-            "all_metrics": [
-                {
-                    "name": "Train and test loss vs epoch",
-                    "time_series": trainTestLoss,
-                    "graph_index": 0,
-                    "chart_type": "LINE",
-                },
-                {
-                    "name": "Confusion matrix",
-                    "values": confusionMatrix.tolist(),
-                    "chart_type": "CONFUSION_MATRIX",
-                    "graph_index": 1,
-                },
-                {
-                    "name": "AUC/ROC curve",
-                    "values": aucRocCurve,
-                    "chart_type": "AUC/ROC",
-                    "graph_index": 2,
-                },
-            ],
+    all_metrics = [
+        {
+            "name": "Train and test loss vs epoch",
+            "time_series": trainTestLoss,
+            "graph_index": 0,
+            "chart_type": "LINE",
         }
+    ]
+    if is_classification:
+        confusionMatrix = trainer.generate_confusion_matrix()
+        aucRocCurve = trainer.generate_AUC_ROC_CURVE()
+        all_metrics.append(
+            {
+                "name": "Confusion matrix",
+                "values": confusionMatrix.tolist(),
+                "chart_type": "CONFUSION_MATRIX",
+                "graph_index": 1,
+            }
+        )
+        all_metrics.append(
+            {
+                "name": "AUC/ROC curve",
+                "values": aucRocCurve,
+                "chart_type": "AUC/ROC",
+                "graph_index": 2,
+            }
+        )
+
+    detailedTrainResultsData = DetailedTrainResultsData(
+        **{"basic_info": basic_info, "all_metrics": all_metrics}
     )
     return detailedTrainResultsData
 
@@ -135,13 +139,6 @@ def tabularTrainTask(input: dict, trainspaceId: str, uid: str):
                 tabularParams.epochs,
                 dataCreator.getCategoryList(),
             )
-
-            detailedTrainResultsData = collectClassificationTrainingResults(
-                trainer, basic_info
-            )
-
-            # save detailedTrainResultsData
-            saveDetailedTrainResultsDataToS3(detailedTrainResultsData)
         else:
             trainer = RegressionTrainer(
                 train_loader,
@@ -151,43 +148,12 @@ def tabularTrainTask(input: dict, trainspaceId: str, uid: str):
                 criterionHandler,
                 tabularParams.epochs,
             )
+        detailedTrainResultsData = collectTrainingResults(
+            trainer, basic_info, tabularParams.problem_type == "CLASSIFICATION"
+        )
 
-            trainTestLoss = [
-                {
-                    "x_name": "Epoch",
-                    "y_name": "Train loss",
-                    "x_values": [],
-                    "y_values": [],
-                },
-                {
-                    "x_name": "Epoch",
-                    "y_name": "Test loss",
-                    "x_values": [],
-                    "y_values": [],
-                },
-            ]
-            for epoch_result in trainer:
-                trainTestLoss[0]["x_values"].append(epoch_result.epoch_num)
-                trainTestLoss[0]["y_values"].append(epoch_result.train_loss)
-                trainTestLoss[1]["x_values"].append(epoch_result.epoch_num)
-                trainTestLoss[1]["y_values"].append(epoch_result.test_loss)
-
-            detailedTrainResultsData = DetailedTrainResultsData(
-                **{
-                    "basic_info": basic_info,
-                    "all_metrics": [
-                        {
-                            "name": "Train and test loss vs epoch",
-                            "time_series": trainTestLoss,
-                            "graph_index": 0,
-                            "chart_type": "LINE",
-                        }
-                    ],
-                }
-            )
-
-            # save detailedTrainResultsData
-            saveDetailedTrainResultsDataToS3(detailedTrainResultsData)
+        # save detailedTrainResultsData
+        saveDetailedTrainResultsDataToS3(detailedTrainResultsData)
 
 
 @celery_app.task(name="imageTrainTask")
@@ -222,9 +188,7 @@ def imageTrainTask(input: dict, trainspaceId: str, uid: str):
                 imageParams.epochs,
                 dataCreator.getCategoryList(),
             )
-            detailedTrainResultsData = collectClassificationTrainingResults(
-                trainer, basic_info
-            )
+            detailedTrainResultsData = collectTrainingResults(trainer, basic_info, True)
 
             # save detailedTrainResultsData
             saveDetailedTrainResultsDataToS3(detailedTrainResultsData)