Formatting, acrolinx fixes, branding fixes

HeidiSteen · HeidiSteen · commit 46ada2735258 · 2022-08-16T14:09:10.000-07:00
diff --git a/articles/cognitive-services/big-data/cognitive-services-for-big-data.md b/articles/cognitive-services/big-data/cognitive-services-for-big-data.md
@@ -14,17 +14,17 @@ ms.author: marhamil
 
 ![Azure Cognitive Services for big data](media/cognitive-services-big-data-overview.svg)
 
-The Azure Cognitive Services for big data lets users channel terabytes of data through Cognitive Services using [Apache Spark&trade;](/dotnet/spark/what-is-spark). With the Cognitive Services for big data, it's easy to create large-scale intelligent applications with any datastore.
+Azure Cognitive Services for big data lets users channel terabytes of data through Cognitive Services using [Apache Spark&trade;](/dotnet/spark/what-is-spark) and open source libraries for distributed machine learning workloads. With Cognitive Services for big data, it's easy to create large-scale intelligent applications with any datastore.
 
-With Cognitive Services for big data you can embed continuously improving, intelligent models directly into Apache Spark&trade; and SQL computations. These tools liberate developers from low-level networking details, so that they can focus on creating smart, distributed applications.
+Using the resources and libraries described in this article, you can embed continuously improving, intelligent models directly into Apache Spark&trade; and SQL computations. These tools liberate developers from low-level networking details, so that they can focus on creating smart, distributed applications.
 
 ## Features and benefits
 
-Cognitive Services for big data can use services from any region in the world, as well as [containerized Cognitive Services](../cognitive-services-container-support.md). Containers support low or no connectivity deployments with ultra-low latency responses. Containerized Cognitive Services can be run locally, directly on the worker nodes of your Spark cluster, or on an external orchestrator like Kubernetes.
+Cognitive Services for big data can use resources from any [supported region](https://azure.microsoft.comglobal-infrastructure/services/?products=cognitive-services), as well as [containerized Cognitive Services](../cognitive-services-container-support.md). Containers support low or no connectivity deployments with ultra-low latency responses. Containerized Cognitive Services can be run locally, directly on the worker nodes of your Spark cluster, or on an external orchestrator like Kubernetes.
 
 ## Supported services
 
-[Cognitive Services](../index.yml), accessed through APIs and SDKs, help developers build intelligent applications without having AI or data science skills. With Cognitive Services you can make your applications see, hear, speak, understand, and reason. To use the Cognitive Services, your application must send data to the service over the network. Once received, the service sends an intelligent response in return. The following services are available for big data workloads:
+[Cognitive Services](../index.yml), accessed through APIs and SDKs, help developers build intelligent applications without having AI or data science skills. With Cognitive Services you can make your applications see, hear, speak, understand, and reason. To use Cognitive Services, your application must send data to the service over the network. Once received, the service sends an intelligent response in return. The following Cognitive Services resources are available for big data workloads:
 
 ### Vision
 
@@ -59,7 +59,7 @@ Cognitive Services for big data can use services from any region in the world, a
 
 ## Supported programming languages for Cognitive Services for big data
 
-The Cognitive Services for big data are built on Apache Spark. Apache Spark is a distributed computing library that supports Java, Scala, Python, R, and many other languages. These languages are currently supported.
+Cognitive Services for big data are built on Apache Spark. Apache Spark is a distributed computing library that supports Java, Scala, Python, R, and many other languages. These languages are currently supported.
 
 ### Python
 
@@ -71,7 +71,7 @@ We provide a Scala and Java-based Spark API in the `com.microsoft.ml.spark.cogni
 
 ## Supported platforms and connectors
 
-The Cognitive Services for big data requires Apache Spark. There are several Apache Spark platforms that support the Cognitive Services for big data.
+Big data scenarios require Apache Spark. There are several Apache Spark platforms that support Cognitive Services for big data.
 
 ### Azure Databricks
 
@@ -100,11 +100,11 @@ The basis of Spark is the DataFrame: a tabular collection of data distributed ac
    - Do SQL-style computations such as join and filter tables.
    - Apply functions to large datasets using MapReduce style parallelism.
    - Apply Distributed Machine Learning using Microsoft Machine Learning for Apache Spark.
-   - Use the Cognitive Services for big data to enrich your data with ready-to-use intelligent services.
+   - Use Cognitive Services for big data to enrich your data with ready-to-use intelligent services.
 
 ### Microsoft Machine Learning for Apache Spark (MMLSpark)
 
-[Microsoft Machine Learning for Apache Spark](https://mmlspark.blob.core.windows.net/website/index.html#install) (MMLSpark) is an open-source, distributed machine learning library (ML) built on Apache Spark. The Cognitive Services for big data is included in this package. Additionally, MMLSpark contains several other ML tools for Apache Spark, such as LightGBM, Vowpal Wabbit, OpenCV, LIME, and more. With MMLSpark, you can build powerful predictive and analytical models from any Spark datasource.
+[Microsoft Machine Learning for Apache Spark](https://mmlspark.blob.core.windows.net/website/index.html#install) (MMLSpark) is an open-source, distributed machine learning library (ML) built on Apache Spark. Cognitive Services for big data is included in this package. Additionally, MMLSpark contains several other ML tools for Apache Spark, such as LightGBM, Vowpal Wabbit, OpenCV, LIME, and more. With MMLSpark, you can build powerful predictive and analytical models from any Spark datasource.
 
 ### HTTP on Spark
 
@@ -126,11 +126,11 @@ Cognitive Services for big data is an example of how we can integrate intelligen
 
 - [The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Services](https://databricks.com/session/the-azure-cognitive-services-on-spark-clusters-with-embedded-intelligent-services)
 - [Spark Summit Keynote: Scalable AI for Good](https://databricks.com/session_eu19/scalable-ai-for-good)
-- [The Cognitive Services for big data in Cosmos DB](https://medius.studios.ms/Embed/Video-nc/B19-BRK3004?latestplayer=true&l=2571.208093)
+- [Cognitive Services for big data in Cosmos DB](https://medius.studios.ms/Embed/Video-nc/B19-BRK3004?latestplayer=true&l=2571.208093)
 - [Lightning Talk on Large Scale Intelligent Microservices](https://www.youtube.com/watch?v=BtuhmdIy9Fk&t=6s)
 
 ## Next steps
 
-- [Getting Started with the Cognitive Services for big data](getting-started.md)
+- [Getting Started with Cognitive Services for big data](getting-started.md)
 - [Simple Python Examples](samples-python.md)
 - [Simple Scala Examples](samples-scala.md)
diff --git a/articles/cognitive-services/big-data/getting-started.md b/articles/cognitive-services/big-data/getting-started.md
@@ -98,7 +98,7 @@ To get started on Azure Kubernetes Service, follow these steps:
 
 ## Try a sample
 
-After you set up your Spark cluster and environment, you can run a short sample. This sample assumes Azure Databricks and the `mmlspark.cognitive` package.
+After you set up your Spark cluster and environment, you can run a short sample. This sample assumes Azure Databricks and the `mmlspark.cognitive` package. For an example using `synapseml.cognitive`, see [Add search to AI-enriched data from Apache Spark using SynapseML](/search/search-synapseml-cognitive-services).
 
 First, you can create a notebook in Azure Databricks. For other Spark cluster providers, use their notebooks or Spark Submit.
 
@@ -114,34 +114,34 @@ First, you can create a notebook in Azure Databricks. For other Spark cluster pr
 
 1. Paste this code snippet into your new notebook.
 
-  ```python
-  from mmlspark.cognitive import *
-  from pyspark.sql.functions import col
-  
-  # Add your region and subscription key from the Language service (or a general Cognitive Service key)
-  # If using a multi-region Cognitive Services resource, delete the placeholder text: service_region = ""
-  service_key = "ADD-SUBSCRIPTION-KEY-HERE"
-  service_region = "ADD-SERVICE-REGION-HERE"
-  
-  df = spark.createDataFrame([
-    ("I am so happy today, its sunny!", "en-US"),
-    ("I am frustrated by this rush hour traffic", "en-US"),
-    ("The cognitive services on spark aint bad", "en-US"),
-  ], ["text", "language"])
-  
-  sentiment = (TextSentiment()
-      .setTextCol("text")
-      .setLocation(service_region)
-      .setSubscriptionKey(service_key)
-      .setOutputCol("sentiment")
-      .setErrorCol("error")
-      .setLanguageCol("language"))
-  
-  results = sentiment.transform(df)
-  
-  # Show the results in a table
-  display(results.select("text", col("sentiment")[0].getItem("score").alias("sentiment")))
-  ```
+    ```python
+    from mmlspark.cognitive import *
+    from pyspark.sql.functions import col
+    
+    # Add your region and subscription key from the Language service (or a general Cognitive Service key)
+    # If using a multi-region Cognitive Services resource, delete the placeholder text: service_region = ""
+    service_key = "ADD-SUBSCRIPTION-KEY-HERE"
+    service_region = "ADD-SERVICE-REGION-HERE"
+    
+    df = spark.createDataFrame([
+      ("I am so happy today, its sunny!", "en-US"),
+      ("I am frustrated by this rush hour traffic", "en-US"),
+      ("The cognitive services on spark aint bad", "en-US"),
+    ], ["text", "language"])
+    
+    sentiment = (TextSentiment()
+        .setTextCol("text")
+        .setLocation(service_region)
+        .setSubscriptionKey(service_key)
+        .setOutputCol("sentiment")
+        .setErrorCol("error")
+        .setLanguageCol("language"))
+    
+    results = sentiment.transform(df)
+    
+    # Show the results in a table
+    display(results.select("text", col("sentiment")[0].getItem("score").alias("sentiment")))
+    ```
 
 1. Get your subscription key from the **Keys and Endpoint** menu from your Language resource in the Azure portal.
 
diff --git a/articles/cognitive-services/big-data/samples-python.md b/articles/cognitive-services/big-data/samples-python.md
@@ -6,7 +6,7 @@ author: mhamilton723
 manager: nitinme
 ms.service: cognitive-services
 ms.topic: sample
-ms.date: 10/28/2021
+ms.date: 08/16/2022
 ms.author: marhamil
 ms.devlang: python
 ---
@@ -294,8 +294,8 @@ display(client.transform(df).select("country", udf(get_response_body)(col("respo
 
 | country   | response |
 |:----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| br | [{"page":1,"pages":1,"per_page":"50","total":1},[{"id":"BRA","iso2Code":"BR","name":"Brazil","region":{"id":"LCN","iso2code":"ZJ","value":"Latin America & Caribbean "},"adminregion":{"id":"LAC","iso2code":"XJ","value":"Latin America & Caribbean (excluding high income)"},"incomeLevel":{"id":"UMC","iso2code":"XT","value":"Upper middle income"},"lendingType":{"id":"IBD","iso2code":"XF","value":"IBRD"},"capitalCity":"Brasilia","longitude":"-47.9292","latitude":"-15.7801"}]] |
-| usa  | [{"page":1,"pages":1,"per_page":"50","total":1},[{"id":"USA","iso2Code":"US","name":"United States","region":{"id":"NAC","iso2code":"XU","value":"North America"},"adminregion":{"id":"","iso2code":"","value":""},"incomeLevel":{"id":"HIC","iso2code":"XD","value":"High income"},"lendingType":{"id":"LNX","iso2code":"XX","value":"Not classified"},"capitalCity":"Washington D.C.","longitude":"-77.032","latitude":"38.8895"}]] |
+| br | `[{"page":1,"pages":1,"per_page":"50","total":1},[{"id":"BRA","iso2Code":"BR","name":"Brazil","region":{"id":"LCN","iso2code":"ZJ","value":"Latin America & Caribbean "},"adminregion":{"id":"LAC","iso2code":"XJ","value":"Latin America & Caribbean (excluding high income)"},"incomeLevel":{"id":"UMC","iso2code":"XT","value":"Upper middle income"},"lendingType":{"id":"IBD","iso2code":"XF","value":"IBRD"},"capitalCity":"Brasilia","longitude":"-47.9292","latitude":"-15.7801"}]]` |
+| usa  | `[{"page":1,"pages":1,"per_page":"50","total":1},[{"id":"USA","iso2Code":"US","name":"United States","region":{"id":"NAC","iso2code":"XU","value":"North America"},"adminregion":{"id":"","iso2code":"","value":""},"incomeLevel":{"id":"HIC","iso2code":"XD","value":"High income"},"lendingType":{"id":"LNX","iso2code":"XX","value":"Not classified"},"capitalCity":"Washington D.C.","longitude":"-77.032","latitude":"38.8895"}]]` |
 
 ## See also
 
