Update aml-pipelines-use-databricks-as-compute-target.ipynb

.vscode/settings.json

@@ -0,0 +1 @@
+{}

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/normalize.py

@@ -28,21 +28,13 @@ replaced_distance_vals_df = (replaced_stfor_vals_df.replace({"distance": ".00"},
 
 normalized_df = replaced_distance_vals_df.astype({"distance": 'float64'})
 
-
-def time_to_us(time_str):
-    hh, mm , ss = map(int, time_str.split(':'))
-    return (ss + 60 * (mm + 60 * hh)) * (10**6)
-
-
 temp = pd.DatetimeIndex(normalized_df["pickup_datetime"])
-normalized_df["pickup_date"] = pd.to_datetime(temp.date)
+normalized_df["pickup_date"] = temp.date
 normalized_df["pickup_time"] = temp.time
-normalized_df["pickup_time"] = normalized_df["pickup_time"].apply(lambda x: time_to_us(str(x)))
 
 temp = pd.DatetimeIndex(normalized_df["dropoff_datetime"])
-normalized_df["dropoff_date"] = pd.to_datetime(temp.date)
+normalized_df["dropoff_date"] = temp.date
 normalized_df["dropoff_time"] = temp.time
-normalized_df["dropoff_time"] = normalized_df["dropoff_time"].apply(lambda x: time_to_us(str(x)))
 
 del normalized_df["pickup_datetime"]
 del normalized_df["dropoff_datetime"]

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb

@@ -272,8 +272,7 @@
         "dependencies:\n",
         "- python=3.6.2\n",
         "- pip:\n",
-        "  - azureml-core\n",
+        "  - azureml-defaults\n",
-        "  - azureml-dataset-runtime\n",
         "  - keras==2.4.3\n",
         "  - tensorflow==2.4.3\n",
         "  - numpy\n",