Merge pull request #208197 from HeidiSteen/heidist-synapse

[cognitive services] Style updates to big data concept and get-started articles

Author: PMEds28

articles/cognitive-services/TOC.yml

@@ -87,7 +87,7 @@
       href: ./security-controls-policy.md
   - name: Use with big data
     items:
-    - name: Cognitive Services for Big Data
+    - name: Cognitive Services for big data
       href: ./big-data/cognitive-services-for-big-data.md
     - name: Getting started
       href: ./big-data/getting-started.md

articles/cognitive-services/big-data/cognitive-services-for-big-data.md

@@ -1,6 +1,6 @@
 ---
-title: "Cognitive Services for Big Data"
-description: Learn how to leverage Azure Cognitive Services on large datasets using Python, Java, and Scala. With Cognitive Services for Big Data you can embed continuously improving, intelligent models directly into Apache Spark™ and SQL computations.
+title: "Cognitive Services for big data"
+description: Learn how to leverage Azure Cognitive Services on large datasets using Python, Java, and Scala. With Cognitive Services for big data you can embed continuously improving, intelligent models directly into Apache Spark™ and SQL computations.
 services: cognitive-services
 author: mhamilton723
 manager: nitinme
@@ -10,21 +10,21 @@ ms.date: 10/28/2021
 ms.author: marhamil
 ---
 
-# Azure Cognitive Services for Big Data
+# Azure Cognitive Services for big data
 
-![Azure Cognitive Services for Big Data](media/cognitive-services-big-data-overview.svg)
+![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™](/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™](/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™ 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™ 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
 
@@ -57,21 +57,35 @@ Cognitive Services for Big Data can use services from any region in the world, a
 |:-----------|:------------------|
 |[Bing Image Search](/azure/cognitive-services/bing-image-search "Bing Image Search")|The Bing Image Search service returns a display of images determined to be relevant to the user's query.|
 
-## Supported programming languages for Cognitive Services for Big Data
+## 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. See [SynapseML](https://microsoft.github.io/SynapseML) for documentation, samples, and blog posts.
+
+The following languages are currently supported.
 
 ### Python
 
-We provide a PySpark API in the `mmlspark.cognitive` namespace of [Microsoft ML for Apache Spark](https://aka.ms/spark). For more information, see the [Python Developer API](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc1/pyspark/mmlspark.cognitive.html). For usage examples, see the [Python Samples](samples-python.md).
+We provide a PySpark API for current and legacy libraries:
+
+* [`synapseml.cognitive`](https://mmlspark.blob.core.windows.net/docs/0.10.0/pyspark/synapse.ml.cognitive.html)
+
+* [`mmlspark.cognitive`](https://mmlspark.blob.core.windows.net/docs/0.18.1/pyspark/modules.html)
+
+For more information, see the [Python Developer API](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc1/pyspark/mmlspark.cognitive.html). For usage examples, see the [Python Samples](samples-python.md).
 
 ### Scala and Java
 
-We provide a Scala and Java-based Spark API in the `com.microsoft.ml.spark.cognitive` namespace of [Microsoft ML for Apache Spark](https://aka.ms/spark). For more information, see the [Scala Developer API](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc1/scala/index.html#package). For usage examples, see the [Scala Samples](samples-scala.md).
+We provide a Scala and Java-based Spark API for current and legacy libraries:
+
+* [`com.microsoft.synapseml.cognitive`](https://mmlspark.blob.core.windows.net/docs/0.10.0/scala/com/microsoft/azure/synapse/ml/cognitive/index.html)
+
+* [`com.microsoft.ml.spark.cognitive`](https://mmlspark.blob.core.windows.net/docs/0.18.1/scala/index.html#com.microsoft.ml.spark.cognitive.package)
+
+For more information, see the [Scala Developer API](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc1/scala/index.html#package). For usage examples, see the [Scala Samples](samples-scala.md).
 
 ## 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
 
@@ -100,15 +114,15 @@ 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
 
-Cognitive Services for Big Data is an example of how we can integrate intelligent web services with big data. Web services power many applications across the globe and most services communicate through the Hypertext Transfer Protocol (HTTP). To work with *arbitrary* web services at large scales, we provide HTTP on Spark. With HTTP on Spark, you can pass terabytes of data through any web service. Under the hood, we use this technology to power Cognitive Services for Big Data.
+Cognitive Services for big data is an example of how we can integrate intelligent web services with big data. Web services power many applications across the globe and most services communicate through the Hypertext Transfer Protocol (HTTP). To work with *arbitrary* web services at large scales, we provide HTTP on Spark. With HTTP on Spark, you can pass terabytes of data through any web service. Under the hood, we use this technology to power Cognitive Services for big data.
 
 ## Developer samples
 
@@ -126,11 +140,11 @@ 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)