fix readme and naming as requested and minor cleanup

samples/ibm-samples/openscale/README.md

@@ -1,6 +1,6 @@
 # Watson OpenScale Example
 
-This simple OpenScale pipeline will demonstrate how to train a model using IBM Spark Service, then deploy it with Watson Machine Learning, and subscribe the model with Watson OpenScale monitoring.
+This simple OpenScale pipeline will demonstrate how to train a model using IBM Spark Service, store and deploy it with Watson Machine Learning, and then use Watson OpenScale for fairness and quality monitoring.
 
 ## Prerequisites
 This pipeline requires the user to have provisioned OpenScale, Spark, and Machine Learning Service on Watson, a cloud object store set up and the service credentials configured in the creds.ini file.
@@ -9,9 +9,9 @@ To provision your own OpenScale, Spark, Watson Machine Learning services and clo
 
 1. IBM Watson Machine Learning service instance
 
-To create a machine learning service, go to [IBM Cloud](https://cloud.ibm.com/), login with IBM account id first. From the `Catalog` page, click on `AI` tab on the left side to go to this [page](https://cloud.ibm.com/catalog?category=ai). Then click on the [`Machine Learning`](https://cloud.ibm.com/catalog/services/machine-learning) link and follow the instructions to create the service.
+To create a Watson Machine Learning service, go to [IBM Cloud](https://cloud.ibm.com/), login with IBM account id first. From the `Catalog` page, click on `AI` tab on the left side to go to this [page](https://cloud.ibm.com/catalog?category=ai). Then click on the [`Machine Learning`](https://cloud.ibm.com/catalog/services/machine-learning) link and follow the instructions to create the service.
 
-Once the service is created, from the service's `Dashboard`, follow the instruction to generate `service credentials`. Refer to IBM Cloud [documents](https://cloud.ibm.com/docs) for help if needed. Collect the `url`, `username`, `password`, `apikey`, and `instance_id` info from the service credentials as these will be required to access the service.
+Once the service is created, from the service's `Dashboard`, follow the instructions to generate `service credentials`. Refer to IBM Cloud [documents](https://cloud.ibm.com/docs) for help if needed. Collect the `url`, `username`, `password`, `apikey`, and `instance_id` info from the service credentials as these will be required to access the service.
 
 2. IBM Watson OpenScale service instance
 
@@ -25,7 +25,7 @@ In addition, collect the IBM Cloud API Key from this [page](https://cloud.ibm.co
 
 The IBM Spark service will provide several spark executors to help train our example model. From the `Catalog` page, click on `Web and Application` tab on the left side to go to this [page](https://cloud.ibm.com/catalog?category=app_services). Then click on the [`Apache Spark`](https://cloud.ibm.com/catalog/services/apache-spark) link and follow the instructions to create the service.
 
-Once the service is created, from the service's `Service credentials` on the left side, follow the instruction to generate `service credentials`. Refer to IBM Cloud [documents](https://cloud.ibm.com/docs) for help if needed.
+Once the service is created, from the service's `Service credentials` on the left side, follow the instructions to generate `service credentials`. Refer to IBM Cloud [documents](https://cloud.ibm.com/docs) for help if needed.
 Collect the `tenant_secret`, `tenant_id`, `cluster_master_url`, and `instance_id` info from the service credentials as these will be required to access the service.
 
 4. A cloud object store
@@ -86,7 +86,7 @@ dsl-compile --py openscale.py --output openscale.tar.gz
 Then, submit `openscale.tar.gz` to the kubeflow pipeline UI. From there you can create different experiments and runs with the OpenScale pipeline.
 
 ## Pipeline Parameters
-- **cos-bucket-name**: Object Storage bucket that has the spark training files and openscale manifest
+- **bucket-name**: Object Storage bucket that has Spark training files and OpenScale manifest
 - **training-data-link**: Link to a public data source if the data is not being preprocessed.
 - **postgres-schema-name**: PostgreSQL schema name for storing model payload metrics
 - **label-name**: Model label name in the dataset.

samples/ibm-samples/openscale/openscale.py

@@ -14,11 +14,11 @@
 
 
 @dsl.pipeline(
-  name='Watson openscale pipeline',
+  name='Watson OpenScale Pipeline',
   description='A pipeline for end to end Spark machine learning workflow and model monitoring.'
 )
 def aiosPipeline(
-    COS_BUCKET_NAME='german-tomcli',
+    BUCKET_NAME='',
     TRAINING_DATA_LINK='https://raw.githubusercontent.com/emartensibm/german-credit/master/german_credit_data_biased_training.csv',
     POSTGRES_SCHEMA_NAME='data_mart_credit',
     LABEL_NAME='Risk',
@@ -34,17 +34,17 @@ def aiosPipeline(
     """A pipeline for Spark machine learning workflow with OpenScale."""
 
     data_preprocess_spark = preprocess_spark_op(
-        bucket_name=COS_BUCKET_NAME,
+        bucket_name=BUCKET_NAME,
         data_url=TRAINING_DATA_LINK
         ).apply(params.use_ai_pipeline_params(secret_name))
     train_spark = train_spark_op(
-        bucket_name=COS_BUCKET_NAME,
+        bucket_name=BUCKET_NAME,
         data_filename=data_preprocess_spark.output,
         model_filename=MODEL_FILE_PATH,
         spark_entrypoint=SPARK_ENTRYPOINT
         ).apply(params.use_ai_pipeline_params(secret_name))
     store_spark_model = store_spark_op(
-        bucket_name=COS_BUCKET_NAME,
+        bucket_name=BUCKET_NAME,
         aios_manifest_path=AIOS_MANIFEST_PATH,
         problem_type=PROBLEM_TYPE,
         model_name=MODEL_NAME,
@@ -64,7 +64,7 @@ def aiosPipeline(
         aios_schema=POSTGRES_SCHEMA_NAME,
         label_column=LABEL_NAME,
         aios_manifest_path=AIOS_MANIFEST_PATH,
-        bucket_name=COS_BUCKET_NAME,
+        bucket_name=BUCKET_NAME,
         problem_type=PROBLEM_TYPE
         ).apply(params.use_ai_pipeline_params(secret_name))
     monitor_quality = quality_op(
@@ -74,7 +74,7 @@ def aiosPipeline(
     monitor_fairness = fairness_op(
         model_name=subscribe.output,
         aios_manifest_path=AIOS_MANIFEST_PATH,
-        cos_bucket_name=COS_BUCKET_NAME,
+        cos_bucket_name=BUCKET_NAME,
         data_filename=data_preprocess_spark.output
         ).apply(params.use_ai_pipeline_params(secret_name))
 

samples/ibm-samples/openscale/source/model.py

@@ -14,9 +14,6 @@
 spark_df = df_data
 (train_data, test_data) = spark_df.randomSplit([0.8, 0.2], 24)
 
-MODEL_NAME = "AIOS Spark German Risk Model - Final"
-DEPLOYMENT_NAME = "AIOS Spark German Risk Deployment - Final"
-
 print("Number of records for training: " + str(train_data.count()))
 print("Number of records for evaluation: " + str(test_data.count()))