update samples from Release-76 as a part of SDK release

Author: amlrelsa-ms

README.md

@@ -1,7 +1,5 @@
 # Azure Machine Learning service example notebooks
 
-> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
-
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
 ![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -460,8 +460,8 @@
         "    name=\"Merge Taxi Data\",\n",
         "    script_name=\"merge.py\", \n",
         "    arguments=[\"--output_merge\", merged_data],\n",
-        "    inputs=[cleansed_green_data.parse_parquet_files(file_extension=None),\n",
-        "            cleansed_yellow_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[cleansed_green_data.parse_parquet_files(),\n",
+        "            cleansed_yellow_data.parse_parquet_files()],\n",
         "    outputs=[merged_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig=aml_run_config,\n",
@@ -497,7 +497,7 @@
         "    name=\"Filter Taxi Data\",\n",
         "    script_name=\"filter.py\", \n",
         "    arguments=[\"--output_filter\", filtered_data],\n",
-        "    inputs=[merged_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[merged_data.parse_parquet_files()],\n",
         "    outputs=[filtered_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -533,7 +533,7 @@
         "    name=\"Normalize Taxi Data\",\n",
         "    script_name=\"normalize.py\", \n",
         "    arguments=[\"--output_normalize\", normalized_data],\n",
-        "    inputs=[filtered_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[filtered_data.parse_parquet_files()],\n",
         "    outputs=[normalized_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -574,7 +574,7 @@
         "    name=\"Transform Taxi Data\",\n",
         "    script_name=\"transform.py\", \n",
         "    arguments=[\"--output_transform\", transformed_data],\n",
-        "    inputs=[normalized_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[normalized_data.parse_parquet_files()],\n",
         "    outputs=[transformed_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -614,7 +614,7 @@
         "    script_name=\"train_test_split.py\", \n",
         "    arguments=[\"--output_split_train\", output_split_train,\n",
         "               \"--output_split_test\", output_split_test],\n",
-        "    inputs=[transformed_data.parse_parquet_files(file_extension=None)],\n",
+        "    inputs=[transformed_data.parse_parquet_files()],\n",
         "    outputs=[output_split_train, output_split_test],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -690,7 +690,7 @@
         "    \"n_cross_validations\": 5\n",
         "}\n",
         "\n",
-        "training_dataset = output_split_train.parse_parquet_files(file_extension=None).keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n",
+        "training_dataset = output_split_train.parse_parquet_files().keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             debug_log = 'automated_ml_errors.log',\n",