diff --git a/label/label-to-review/src/main/java/com/kamco/cd/kamcoback/scheduler/service/TrainingDataLabelJobService.java b/label/label-to-review/src/main/java/com/kamco/cd/kamcoback/scheduler/service/TrainingDataLabelJobService.java
index 375b95f..a5f3336 100644
--- a/label/label-to-review/src/main/java/com/kamco/cd/kamcoback/scheduler/service/TrainingDataLabelJobService.java
+++ b/label/label-to-review/src/main/java/com/kamco/cd/kamcoback/scheduler/service/TrainingDataLabelJobService.java
@@ -33,69 +33,85 @@ public class TrainingDataLabelJobService {
     return "local".equalsIgnoreCase(profile);
   }
 
-  @Scheduled(cron = "0 * * * * *")
   public void runTask() {
     // 프록시를 통해 호출해야 @Transactional이 적용됨
     applicationContext
-        .getBean(TrainingDataLabelJobService.class)
-        .assignReviewerYesterdayLabelComplete(null);
+      .getBean(TrainingDataLabelJobService.class)
+      .assignReviewerYesterdayLabelComplete(null);
   }
 
   @Transactional
   public void assignReviewerYesterdayLabelComplete(LocalDate baseDate) {
 
-    //    if (isLocalProfile()) {
-    //      return;
-    //    }
-
     try {
+      log.info("[Step 1-1] 라벨링 완료된 데이터 목록 조회한다.");
+      log.info("=== baseDate : {}", baseDate);
+      log.info("=== baseDate 있으면 해당 일자, 없으면 어제일자로 조회");
       List<Tasks> tasks =
-          trainingDataLabelJobCoreService.findCompletedYesterdayUnassigned(baseDate);
+        trainingDataLabelJobCoreService.findCompletedYesterdayUnassigned(baseDate);
 
+      log.info("[Step 1-2] 목록 객체 건수 count : {}", tasks == null ? 0 : tasks.size());
       if (tasks.isEmpty()) {
+        log.info("[Step 1-3] 조회된 것 없어 return");
         return;
       }
 
       // 회차별로 그룹핑
+      log.info("[Step 2-1] 회차별로 그룹핑 시작");
       Map<Long, List<Tasks>> taskByRound =
-          tasks.stream().collect(Collectors.groupingBy(Tasks::getAnalUid));
+        tasks.stream().collect(Collectors.groupingBy(Tasks::getAnalUid));
 
       // 회차별 분배
+      log.info("[Step 3-1] 회차별로 분배 시작");
       for (Map.Entry<Long, List<Tasks>> entry : taskByRound.entrySet()) {
         Long analUid = entry.getKey();
         List<Tasks> analTasks = entry.getValue();
 
         // pending 계산
+        log.info("[Step 3-2] 수행하는 회차 analUid: {}", analUid);
+        log.info("해당 회차에 라벨링 할당받은 검수자별 완료 건수 count(), 완료한 게 적은 순으로 해야 일이 한 사람에게 몰리지 않음");
         List<InspectorPendingDto> pendings =
-            trainingDataLabelJobCoreService.findInspectorPendingByRound(analUid);
+          trainingDataLabelJobCoreService.findInspectorPendingByRound(analUid);
 
+        log.info("검수자 수: {}", pendings == null ? 0 : pendings.size());
         if (pendings.isEmpty()) {
+          log.info("[Step 3-3] 할당된 검수자가 없으면 return");
           continue;
         }
 
+        log.info("[Step 4-1] 검수자 사번 List 생성");
         List<String> reviewerIds =
-            pendings.stream().map(InspectorPendingDto::getInspectorUid).toList();
+          pendings.stream().map(InspectorPendingDto::getInspectorUid).toList();
 
         // Lock 걸릴 수 있기 때문에 엔티티 조회하는 Repository 에서 구현
+        log.info("[Step 4-2] 검수자 테이블 lock 걸리지 않게 처리");
         trainingDataLabelJobCoreService.lockInspectors(analUid, reviewerIds);
 
         // 균등 분배
+        log.info("[Step 5-1] 검수자에게 라벨 작업 균등분배 시작");
         Map<String, List<Tasks>> assignMap = distributeByLeastPending(analTasks, reviewerIds);
+        log.info("[Step 5-2] 검수자에게 라벨 작업 균등분배 완료");
 
         // reviewer별 batch update
+        log.info("[Step 5-3] 검수자별 할당 데이터를 batch update 시작");
         assignMap.forEach(
-            (reviewerId, assignedTasks) -> {
-              if (assignedTasks.isEmpty()) {
-                return;
-              }
+          (reviewerId, assignedTasks) -> {
+            if (assignedTasks.isEmpty()) {
+              log.info("[Step 5-4] 할당된 데이터 없으면 return");
+              return;
+            }
 
-              List<UUID> assignmentUids =
-                  assignedTasks.stream().map(Tasks::getAssignmentUid).toList();
-              trainingDataLabelJobCoreService.assignReviewerBatch(assignmentUids, reviewerId);
+            List<UUID> assignmentUids =
+              assignedTasks.stream().map(Tasks::getAssignmentUid).toList();
+            log.info("[Step 6-1] 할당 작업에 검수자 아이디 update");
+            log.info("==== 검수자 사번: {}", reviewerId);
+            log.info("==== 할당 갯수: {}", assignmentUids == null ? 0 : assignmentUids.size());
+            trainingDataLabelJobCoreService.assignReviewerBatch(assignmentUids, reviewerId);
 
-              List<Long> geomUids = assignedTasks.stream().map(Tasks::getInferenceUid).toList();
-              trainingDataLabelJobCoreService.updateGeomUidTestState(geomUids);
-            });
+            log.info("[Step 7-1] geom 테이블에 검수 상태 update");
+            List<Long> geomUids = assignedTasks.stream().map(Tasks::getInferenceUid).toList();
+            trainingDataLabelJobCoreService.updateGeomUidTestState(geomUids);
+          });
       }
     } catch (Exception e) {
       log.error("배치 처리 중 예외", e);
@@ -104,7 +120,7 @@ public class TrainingDataLabelJobService {
   }
 
   private Map<String, List<Tasks>> distributeByLeastPending(
-      List<Tasks> tasks, List<String> reviewerIds) {
+    List<Tasks> tasks, List<String> reviewerIds) {
     Map<String, List<Tasks>> result = new LinkedHashMap<>();
 
     // 순서 유지 중요 (ASC 정렬된 상태)
diff --git a/label/label-to-review/src/main/resources/application-prod.yml b/label/label-to-review/src/main/resources/application-prod.yml
index 1d17099..471febd 100644
--- a/label/label-to-review/src/main/resources/application-prod.yml
+++ b/label/label-to-review/src/main/resources/application-prod.yml
@@ -84,28 +84,28 @@ mapsheet:
     baseurl: /app/detect/result #현재사용안함
 
 file:
-  sync-root-dir: /data/images/
-  sync-tmp-dir: /data/repo/tmp       # image upload temp dir
+  sync-root-dir: /kamco-nfs/images/
+  sync-tmp-dir: /kamco-nfs/repo/tmp       # image upload temp dir
   sync-file-extention: tfw,tif
 
-  #dataset-dir: D:/data/model_output/ #변경 model_output
-  dataset-dir: /data/model_output/export/  # 마운트경로 AI 추론결과
+  #dataset-dir: D:/kamco-nfs/model_output/ #변경 model_output
+  dataset-dir: /kamco-nfs/model_output/export/  # 마운트경로 AI 추론결과
   dataset-tmp-dir: ${file.dataset-dir}tmp/
 
-  #model-dir: D:/data/ckpt/model/
-  model-dir: /data/ckpt/model/             # 학습서버에서 트레이닝한 모델업로드경로
+  #model-dir: D:/kamco-nfs/ckpt/model/
+  model-dir: /kamco-nfs/ckpt/model/             # 학습서버에서 트레이닝한 모델업로드경로
   model-tmp-dir: ${file.model-dir}tmp/
   model-file-extention: pth,json,py
 
-  pt-path: /data/ckpt/v6-cls-checkpoints/
+  pt-path: /kamco-nfs/ckpt/v6-cls-checkpoints/
   pt-FileName: yolov8_6th-6m.pt
-  dataset-response: /data/dataset/response/
+  dataset-response: /kamco-nfs/dataset/response/
 
 inference:
   url: http://127.0.0.1:8000/jobs
   batch-url: http://127.0.0.1:8000/batches
-  geojson-dir: /data/requests/                 # 학습서버에서 트레이닝한 모델업로드경로
-  jar-path: /data/repo/jar/shp-exporter.jar    # 추론실행을 위한 파일생성경로
+  geojson-dir: /kamco-nfs/requests/                 # 학습서버에서 트레이닝한 모델업로드경로
+  jar-path: /kamco-nfs/repo/jar/shp-exporter.jar    # 추론실행을 위한 파일생성경로
   inference-server-name: server1,server2,server3,server4
 
 gukyuin:
@@ -113,7 +113,7 @@ gukyuin:
   cdi: ${gukyuin.url}/api/kcd/cdi
 
 training-data:
-  geojson-dir: /data/dataset/request/
+  geojson-dir: /kamco-nfs/dataset/request/
 
 layer:
   geoserver-url: https://kamco.geo-dev.gs.dabeeo.com