Update to include background thread for production endpoint

Author: brightsparc

build_pipeline/drift-detection.ipynb

@@ -487,8 +487,8 @@
     "\n",
     "\n",
     "dataset_location = get_latest_processed_data()\n",
-    "S3Downloader().download(dataset_location, \"prerocessed\")\n",
-    "df = pd.read_csv(\"prerocessed/validation.csv\", header=None)\n",
+    "S3Downloader().download(dataset_location, \"preprocessed\")\n",
+    "df = pd.read_csv(\"preprocessed/validation.csv\", header=None)\n",
     "\n",
     "# Changing the distribution of data to artificially cause an alarm\n",
     "df[1] = random.choices([1, 2, 3, 4, 5, 6], weights=[2, 1, 2, 5, 2, 1], k=df.shape[0])\n",
@@ -500,7 +500,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then make a series of prediction requests with this data to cause an artificial model monitoring alarm to be triggered.  This will take approx 30 seconds."
+    "Then make a series of prediction requests in the background every 10 minutes with this  data to cause an artificial model monitoring alarm to be triggered."
    ]
   },
   {
@@ -509,10 +509,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%time\n",
-    "endpoint_requests = 1000\n",
-    "for i in range(endpoint_requests):\n",
-    "    predictor.predict(data=tweaked_rows[i % len(tweaked_rows)])"
+    "from threading import Thread\n",
+    "\n",
+    "\n",
+    "def invoke_endpoint_forever():\n",
+    "    while True:\n",
+    "        for i in range(10000):\n",
+    "            predictor.predict(data=tweaked_rows[i % len(tweaked_rows)])\n",
+    "        time.sleep(10 * 60)\n",
+    "\n",
+    "\n",
+    "Thread(target=invoke_endpoint_forever).start()"
    ]
   },
   {