diff --git a/07-train-pipeline.ipynb b/07-train-pipeline.ipynb
index c4c8a1e..282e93f 100644
--- a/07-train-pipeline.ipynb
+++ b/07-train-pipeline.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 2,
    "id": "ffea60e3-5cbe-43b6-bcea-9d83c3e162ff",
    "metadata": {},
    "outputs": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 3,
    "id": "cec2257a-f493-42c7-9429-7543af864ff7",
    "metadata": {},
    "outputs": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 4,
    "id": "a06fbe24-6018-4d6b-8f81-42516b880696",
    "metadata": {},
    "outputs": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 5,
    "id": "79aa47fd-8aa6-4126-b167-bfc678c94e60",
    "metadata": {},
    "outputs": [],
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 6,
    "id": "68a4fb97-bed6-4533-92d3-dbc79e134f5c",
    "metadata": {},
    "outputs": [],
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 7,
    "id": "0b0bdf3f-6834-4ae0-ab0e-18e64e58031e",
    "metadata": {},
    "outputs": [],
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 8,
    "id": "fd32b85b-b44a-4e17-9c23-98fad8efe064",
    "metadata": {},
    "outputs": [
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 9,
    "id": "3868029b-b1ce-45a9-aade-ebdfec8f110f",
    "metadata": {
     "tags": []
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 10,
    "id": "aa1292ae-b51d-42bc-83c5-996e827c2633",
    "metadata": {},
    "outputs": [],
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 11,
    "id": "6893f359-64de-458e-a964-d42ce581fd86",
    "metadata": {},
    "outputs": [],
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 12,
    "id": "b8ab9fab-7a7e-4d7b-b241-19f4d39eb266",
    "metadata": {},
    "outputs": [
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 13,
    "id": "bc26d281-a72f-4825-a5f8-33ff35d2fb03",
    "metadata": {},
    "outputs": [
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 14,
    "id": "bf697751-337f-4757-a5b4-fedc2246cc66",
    "metadata": {},
    "outputs": [
@@ -844,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 15,
    "id": "b7a65b44-3ad4-4168-af40-67289000a434",
    "metadata": {},
    "outputs": [
@@ -961,7 +961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 16,
    "id": "758b11c7-0595-4787-a238-11bcbf3378ce",
    "metadata": {},
    "outputs": [
@@ -1068,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 17,
    "id": "fa1f7c39-6fd8-43c0-ab6a-21fabcd57da4",
    "metadata": {},
    "outputs": [
@@ -1156,7 +1156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 18,
    "id": "e00bf2fa-bc5c-4289-b6a0-7cf3b6e525f3",
    "metadata": {},
    "outputs": [
@@ -1244,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 19,
    "id": "f2a60680-96c2-4aaa-bea9-579bc26f6edf",
    "metadata": {},
    "outputs": [
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 20,
    "id": "f073f3ad-c410-4c08-bd50-1b3ffcbe728c",
    "metadata": {},
    "outputs": [
@@ -1443,7 +1443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 21,
    "id": "050e6925-788b-4760-b659-ef212c3270ff",
    "metadata": {},
    "outputs": [
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 22,
    "id": "c88bec31-9f44-4d8b-a2bd-fce98bfe8c7b",
    "metadata": {},
    "outputs": [
@@ -1831,9 +1831,17 @@
     "    logging.info(f\"endpoint test complete - {count} predictions sent\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c4942c99-bcd0-4a85-be7b-7bb0d19aab46",
+   "metadata": {},
+   "source": [
+    "## Send skewed traffic"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 23,
    "id": "6061d9d3-825a-47e5-ba53-dbae1a9b4b70",
    "metadata": {},
    "outputs": [
@@ -1938,8 +1946,28 @@
     "    _endpoint = vertex_ai.Endpoint(_endpoint_uri)\n",
     "    logging.info(f\"_endpoint defined\")\n",
     "    \n",
+    "    # ===================================================\n",
+    "    # load test instance\n",
+    "    # ===================================================\n",
+    "    LOCAL_INSTANCE_FILE = 'test_instance_list.pkl'\n",
+    "    GCS_PATH_TO_BLOB = f'{experiment_name}/{experiment_run}/{many_test_instances_gcs_filename}'\n",
+    "    LOADED_TEST_LIST = f'loaded_{LOCAL_INSTANCE_FILE}'\n",
+    "    \n",
+    "    loaded_test_instance = download_blob(\n",
+    "        bucket_name=train_output_gcs_bucket,\n",
+    "        source_gcs_obj=GCS_PATH_TO_BLOB,\n",
+    "        local_filename=LOADED_TEST_LIST\n",
+    "    )\n",
+    "    logging.info(f'loaded_test_instance: {loaded_test_instance}')\n",
+    "    \n",
+    "    filehandler = open(LOADED_TEST_LIST, 'rb')\n",
+    "    LIST_OF_DICTS = pkl.load(filehandler)\n",
+    "    filehandler.close()\n",
+    "    \n",
+    "    logging.info(f'len(LIST_OF_DICTS): {len(LIST_OF_DICTS)}')\n",
+    "    \n",
     "    # ====================================================\n",
-    "    # Send skewed predictions\n",
+    "    # load skew features stats\n",
     "    # ====================================================\n",
     "    SKEW_FEATURES_STATS_FILE = 'skew_feat_stats.pkl'\n",
     "    GCS_PATH_TO_BLOB = f'{experiment_name}/{experiment_run}/{SKEW_FEATURES_STATS_FILE}'\n",
@@ -1953,9 +1981,9 @@
     "    )\n",
     "    logging.info(f'loaded_skew_test_instance: {loaded_skew_test_instance}')\n",
     "    \n",
-    "    filehandler = open(LOADED_SKEW_FEATURES_STATS_FILE, 'rb')\n",
-    "    SKEW_FEATURES = pkl.load(filehandler)\n",
-    "    filehandler.close()\n",
+    "    filehandler_v2 = open(LOADED_SKEW_FEATURES_STATS_FILE, 'rb')\n",
+    "    SKEW_FEATURES = pkl.load(filehandler_v2)\n",
+    "    filehandler_v2.close()\n",
     "    \n",
     "    mean_durations, std_durations = SKEW_FEATURES['pl_duration_ms_new']\n",
     "    mean_num_songs, std_num_songs = SKEW_FEATURES['num_pl_songs_new']\n",
@@ -2011,7 +2039,7 @@
     "    # send skewed traffic\n",
     "    monitoring_test(\n",
     "        endpoint=_endpoint, \n",
-    "        instances=LIST_OF_INSTANCES,\n",
+    "        instances=LIST_OF_DICTS,\n",
     "        skew_feat_stat=SKEW_FEATURES,\n",
     "        start=2, \n",
     "        end=8\n",
@@ -2028,7 +2056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 24,
    "id": "53209e5c-2050-4333-a994-4133b8ee58b5",
    "metadata": {},
    "outputs": [
@@ -2305,7 +2333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 25,
    "id": "18a86934-2700-4ce3-b0c9-673fdce5a430",
    "metadata": {},
    "outputs": [
@@ -2342,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 26,
    "id": "8a0a9956-fb25-4d7a-9754-97d5868f7ffd",
    "metadata": {},
    "outputs": [
@@ -2388,7 +2416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 27,
    "id": "28d8b91d-9772-49da-b7a4-4ab33a12c0c4",
    "metadata": {},
    "outputs": [
@@ -2426,7 +2454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 28,
    "id": "cc68e120-39c8-4974-92a2-228cd0173853",
    "metadata": {},
    "outputs": [
@@ -2458,7 +2486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 29,
    "id": "b3beb06e-05a8-470a-9d54-ff02a03758a9",
    "metadata": {},
    "outputs": [
@@ -2489,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 30,
    "id": "f9c1846e-e63f-4c63-8c9c-049f99d503c3",
    "metadata": {},
    "outputs": [
@@ -2521,7 +2549,7 @@
        " 'track_time_signature_can': FixedLenFeature(shape=(), dtype=tf.string, default_value=None)}"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2533,7 +2561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 31,
    "id": "ab61ac70-9da5-4ed6-b1f8-aecc167e8192",
    "metadata": {},
    "outputs": [],
@@ -2563,7 +2591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 32,
    "id": "84158b9e-45d9-4ed3-802d-a2eab948535a",
    "metadata": {},
    "outputs": [],
@@ -2576,7 +2604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 33,
    "id": "75b11787-0c10-4f3b-b76f-0b10dbfd5868",
    "metadata": {},
    "outputs": [],
@@ -2609,7 +2637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 34,
    "id": "cf9ff3ab-ed67-4b0f-90f9-ed97c1e88a58",
    "metadata": {},
    "outputs": [
@@ -2634,7 +2662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 35,
    "id": "6587613f-0351-4fa2-8fd2-aeed12b1eab1",
    "metadata": {},
    "outputs": [],
@@ -2653,7 +2681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 36,
    "id": "8bc940d1-aecb-480b-9b00-cb5c3afcda81",
    "metadata": {},
    "outputs": [
@@ -2688,7 +2716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 37,
    "id": "aef27ee8-e094-4293-a14d-23e87f687a75",
    "metadata": {},
    "outputs": [
@@ -2777,7 +2805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 38,
    "id": "c29169fd-6a15-48a4-99f1-c4bdfb754f89",
    "metadata": {},
    "outputs": [
@@ -2815,7 +2843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 39,
    "id": "d8fa7a02-6d4d-43ba-bce4-5289f86db2ce",
    "metadata": {},
    "outputs": [
@@ -2937,7 +2965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 40,
    "id": "e4d826dc-0dfc-44de-99f1-79d68ecd559d",
    "metadata": {},
    "outputs": [
@@ -2971,7 +2999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 41,
    "id": "f18863e6-1940-468a-adcf-985d879bb21a",
    "metadata": {},
    "outputs": [
@@ -2981,7 +3009,7 @@
        "'gs://ndr-v1-hybrid-vertex-bucket/tfrs-pipe-v1/run-20230926-120445'"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3011,7 +3039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 42,
    "id": "913e0f5d-abc0-4d78-915e-7a28489e3d23",
    "metadata": {},
    "outputs": [
@@ -3051,7 +3079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 43,
    "id": "41530012-e7c5-4574-b1e5-e40b51a1f90b",
    "metadata": {},
    "outputs": [
@@ -3093,7 +3121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 44,
    "id": "f1eb197b-ea56-4b83-aa92-8198a75711ee",
    "metadata": {},
    "outputs": [
@@ -3124,7 +3152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 45,
    "id": "5e6f5b61-ab53-4619-b854-8c5a4da210e4",
    "metadata": {},
    "outputs": [],
@@ -3516,10 +3544,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 46,
    "id": "65c65526-e42c-459c-a3a2-db8c84b6cc43",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyter/.local/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py:1293: FutureWarning: APIs imported from the v1 namespace (e.g. kfp.dsl, kfp.components, etc) will not be supported by the v2 compiler since v2.0.0\n",
+      "  category=FutureWarning,\n"
+     ]
+    }
+   ],
    "source": [
     "# ! rm -f custom_container_pipeline_spec.json\n",
     "\n",
@@ -3542,7 +3579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 47,
    "id": "b3c4df38-23c9-449c-bbd2-bb6432d6e6a6",
    "metadata": {},
    "outputs": [
@@ -3565,7 +3602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 48,
    "id": "c6dba00a-d42b-40f5-8135-777caaae79a2",
    "metadata": {},
    "outputs": [
diff --git a/README.md b/README.md
index cb68a76..eb0425d 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ The end to end example (with public data) follows this architecture:
 
 ![](img/arch.png)
 
-### Notebook Overview
+### Notebook Overview - TODO: needs update
 
 0. [00-load-core-data-to-bq](00-load-core-data-to-bq.ipynb) Extract from the zip file and upload to BQ. This notebook then enriches features for the playlist songs
 
diff --git a/src/train_pipes/send_skewed_traffic.py b/src/train_pipes/send_skewed_traffic.py
new file mode 100644
index 0000000..7900459
--- /dev/null
+++ b/src/train_pipes/send_skewed_traffic.py
@@ -0,0 +1,189 @@
+
+import kfp
+from typing import Any, Callable, Dict, NamedTuple, Optional, List
+from kfp.v2.dsl import (
+    Artifact, Dataset, Input, InputPath, 
+    Model, Output, OutputPath, component, Metrics
+)
+@kfp.v2.dsl.component(
+    base_image="python:3.9",
+    packages_to_install=[
+        'google-cloud-aiplatform==1.26.1',
+        'google-cloud-pipeline-components',
+        'google-cloud-storage',
+        'tensorflow==2.11.0',
+        'numpy'
+    ],
+)
+def send_skewed_traffic(
+    project: str,
+    location: str,
+    version: str,
+    train_output_gcs_bucket: str,
+    experiment_name: str,
+    experiment_run: str,
+    endpoint: str, # Input[Artifact],
+    # feature_dict: dict,
+    # metrics: Output[Metrics],
+):
+    
+    import logging
+    from datetime import datetime
+    import time
+    import numpy as np
+    import pickle as pkl
+    
+    from google.cloud import aiplatform as vertex_ai
+    
+    from google.cloud import storage
+    from google.cloud.storage.bucket import Bucket
+    from google.cloud.storage.blob import Blob
+    
+    from google.protobuf import json_format
+    from google.protobuf.json_format import Parse
+    from google.protobuf.struct_pb2 import Value
+    from google_cloud_pipeline_components.proto.gcp_resources_pb2 import GcpResources
+
+    import tensorflow as tf
+
+    logging.getLogger().setLevel(logging.INFO)
+
+    vertex_ai.init(
+        project=project,
+        location=location,
+    )
+    storage_client = storage.Client(project=project)
+    
+    # ====================================================
+    # helper functions
+    # ====================================================
+    def download_blob(bucket_name, source_gcs_obj, local_filename):
+        """Uploads a file to the bucket."""
+        # storage_client = storage.Client(project=project_number)
+        bucket = storage_client.bucket(bucket_name)
+        blob = bucket.blob(source_gcs_obj)
+        blob.download_to_filename(local_filename)
+        
+        filehandler = open(f'{local_filename}', 'rb')
+        loaded_dict = pkl.load(filehandler)
+        filehandler.close()
+        
+        logging.info(f"File {local_filename} downloaded from gs://{bucket_name}/{source_gcs_obj}")
+        
+        return loaded_dict
+    
+    # ====================================================
+    # get deployed model endpoint
+    # ====================================================
+    logging.info(f"Endpoint = {endpoint}")
+    gcp_resources = Parse(endpoint, GcpResources())
+    logging.info(f"gcp_resources = {gcp_resources}")
+    
+    _endpoint_resource = gcp_resources.resources[0].resource_uri
+    logging.info(f"_endpoint_resource = {_endpoint_resource}")
+    
+    _endpoint_uri = "/".join(_endpoint_resource.split("/")[-8:-2])
+    logging.info(f"_endpoint_uri = {_endpoint_uri}")
+    
+    # define endpoint resource in component
+    _endpoint = vertex_ai.Endpoint(_endpoint_uri)
+    logging.info(f"_endpoint defined")
+    
+    # ===================================================
+    # load test instance
+    # ===================================================
+    LOCAL_INSTANCE_FILE = 'test_instance_list.pkl'
+    GCS_PATH_TO_BLOB = f'{experiment_name}/{experiment_run}/{many_test_instances_gcs_filename}'
+    LOADED_TEST_LIST = f'loaded_{LOCAL_INSTANCE_FILE}'
+    
+    loaded_test_instance = download_blob(
+        bucket_name=train_output_gcs_bucket,
+        source_gcs_obj=GCS_PATH_TO_BLOB,
+        local_filename=LOADED_TEST_LIST
+    )
+    logging.info(f'loaded_test_instance: {loaded_test_instance}')
+    
+    filehandler = open(LOADED_TEST_LIST, 'rb')
+    LIST_OF_DICTS = pkl.load(filehandler)
+    filehandler.close()
+    
+    logging.info(f'len(LIST_OF_DICTS): {len(LIST_OF_DICTS)}')
+    
+    # ====================================================
+    # load skew features stats
+    # ====================================================
+    SKEW_FEATURES_STATS_FILE = 'skew_feat_stats.pkl'
+    GCS_PATH_TO_BLOB = f'{experiment_name}/{experiment_run}/{SKEW_FEATURES_STATS_FILE}'
+    LOADED_SKEW_FEATURES_STATS_FILE = f"loaded_{SKEW_FEATURES_STATS_FILE}"
+    logging.info(f'loading: {LOADED_SKEW_FEATURES_STATS_FILE}')
+    
+    loaded_skew_test_instance = download_blob(
+        bucket_name=train_output_gcs_bucket,
+        source_gcs_obj=GCS_PATH_TO_BLOB,
+        local_filename=LOADED_SKEW_FEATURES_STATS_FILE
+    )
+    logging.info(f'loaded_skew_test_instance: {loaded_skew_test_instance}')
+    
+    filehandler_v2 = open(LOADED_SKEW_FEATURES_STATS_FILE, 'rb')
+    SKEW_FEATURES = pkl.load(filehandler_v2)
+    filehandler_v2.close()
+    
+    mean_durations, std_durations = SKEW_FEATURES['pl_duration_ms_new']
+    mean_num_songs, std_num_songs = SKEW_FEATURES['num_pl_songs_new']
+    mean_num_artists, std_num_artists = SKEW_FEATURES['num_pl_artists_new']
+    mean_num_albums, std_num_albums = SKEW_FEATURES['num_pl_albums_new']
+    
+    logging.info(f"std_durations   : {round(std_durations, 0)}")
+    logging.info(f"std_num_songs   : {round(std_num_songs, 0)}")
+    logging.info(f"std_num_artists : {round(std_num_artists, 0)}")
+    logging.info(f"std_num_albums  : {round(std_num_albums, 0)}\n")
+    
+    def monitoring_test(endpoint, instances, skew_feat_stat, start=2, end=4):
+
+        mean_durations, std_durations = skew_feat_stat['pl_duration_ms_new']
+        mean_num_songs, std_num_songs = skew_feat_stat['num_pl_songs_new']
+        mean_num_artists, std_num_artists = skew_feat_stat['num_pl_artists_new']
+        mean_num_albums, std_num_albums = skew_feat_stat['num_pl_albums_new']
+        
+        logging.info(f"std_durations   : {round(std_durations, 0)}")
+        logging.info(f"std_num_songs   : {round(std_num_songs, 0)}")
+        logging.info(f"std_num_artists : {round(std_num_artists, 0)}")
+        logging.info(f"std_num_albums  : {round(std_num_albums, 0)}\n")
+
+        total_preds = 0
+
+        for multiplier in range(start, end+1):
+
+            print(f"multiplier: {multiplier}")
+
+            pred_count = 0
+
+            for example in instances:
+                list_dict = {}
+
+                example['pl_duration_ms_new'] = round(std_durations * multiplier, 0)
+                example['num_pl_songs_new'] = round(std_num_songs * multiplier, 0)
+                example['num_pl_artists_new'] = round(std_num_artists * multiplier, 0)
+                example['num_pl_albums_new'] = round(std_num_albums * multiplier, 0)
+                # list_of_skewed_instances.append(example)
+
+                response = endpoint.predict(instances=[example])
+
+                if pred_count > 0 and pred_count % 250 == 0:
+                    print(f"pred_count: {pred_count}")
+
+                pred_count += 1
+                total_preds += 1
+
+            logging.info(f"sent {pred_count} pred requests with {multiplier}X multiplier")
+
+        logging.info(f"sent {total_preds} total pred requests")
+        
+    # send skewed traffic
+    monitoring_test(
+        endpoint=_endpoint, 
+        instances=LIST_OF_DICTS,
+        skew_feat_stat=SKEW_FEATURES,
+        start=2, 
+        end=8
+    )