fix build warnings

Author: v-kents

.openpublishing.redirection.json

@@ -105,12 +105,12 @@ Follow [this tutorial](../spark/apache-spark-microsoft-cognitive-toolkit.md) to
 
 Apache Hive and Azure Machine Learning
 
-* [Apache Hive and Azure Machine Learning end-to-end](../../machine-learning/team-data-science-process/hive-walkthrough.md)
-* [Using an Azure HDInsight Hadoop Cluster on a 1-TB dataset](../../machine-learning/team-data-science-process/hive-criteo-walkthrough.md)
+* [Apache Hive and Azure Machine Learning end-to-end](/azure/architecture/data-science-process/hive-walkthrough)
+* [Using an Azure HDInsight Hadoop Cluster on a 1-TB dataset](/azure/architecture/data-science-process/hive-criteo-walkthrough)
 
 Apache Spark and MLLib
 
-* [Machine learning with Apache Spark on HDInsight](../../machine-learning/team-data-science-process/spark-overview.md)
+* [Machine learning with Apache Spark on HDInsight](/azure/architecture/data-science-process/spark-overview)
 * [Apache Spark with Machine Learning: Use Apache Spark in HDInsight for analyzing building temperature using HVAC data](../spark/apache-spark-ipython-notebook-machine-learning.md)
 * [Apache Spark with Machine Learning: Use Apache Spark in HDInsight to predict food inspection results](../spark/apache-spark-machine-learning-mllib-ipython.md)
 

articles/hdinsight/hadoop/apache-hadoop-deep-dive-advanced-analytics.md

@@ -23,7 +23,7 @@ Azure Machine Learning provides tools to model predictive analytics, and a fully
 
 :::image type="content" source="./media/hdinsight-machine-learning-overview/azure-machine-learning.png" alt-text="Microsoft Azure machine learning overview" border="false":::
 
-Create features for data in an HDInsight Hadoop cluster using [Hive queries](../machine-learning/team-data-science-process/create-features-hive.md). *Feature engineering* attempts to increase the predictive power of learning algorithms by creating features from raw data that facilitate the learning process. You can run HiveQL queries from Azure Machine Learning Studio (classic), and access data processed in Hive and stored in blob storage, by using the [Import Data module](../machine-learning/classic/import-data.md).
+Create features for data in an HDInsight Hadoop cluster using [Hive queries](/azure/architecture/data-science-process/create-features-hive). *Feature engineering* attempts to increase the predictive power of learning algorithms by creating features from raw data that facilitate the learning process. You can run HiveQL queries from Azure Machine Learning Studio (classic), and access data processed in Hive and stored in blob storage, by using the [Import Data module](../machine-learning/classic/import-data.md).
 
 ## Microsoft Cognitive Toolkit
 
@@ -38,9 +38,9 @@ To help advance its own work in deep learning, Microsoft  developed the free, ea
 * [Apache Spark with Machine Learning: Use Spark in HDInsight for analyzing building temperature using HVAC data](spark/apache-spark-ipython-notebook-machine-learning.md)
 * [Apache Spark with Machine Learning: Use Spark in HDInsight to predict food inspection results](spark/apache-spark-machine-learning-mllib-ipython.md)
 * [Generate movie recommendations with Apache Mahout](hadoop/apache-hadoop-mahout-linux-mac.md)
-* [Apache Hive and Azure Machine Learning](../machine-learning/team-data-science-process/create-features-hive.md)
-* [Apache Hive and Azure Machine Learning end-to-end](../machine-learning/team-data-science-process/hive-walkthrough.md)
-* [Machine learning with Apache Spark on HDInsight](../machine-learning/team-data-science-process/spark-overview.md)
+* [Apache Hive and Azure Machine Learning](/azure/architecture/data-science-process/create-features-hive)
+* [Apache Hive and Azure Machine Learning end-to-end](/azure/architecture/data-science-process/hive-walkthrough)
+* [Machine learning with Apache Spark on HDInsight](/azure/architecture/data-science-process/spark-overview)
 
 ### Deep learning resources
 

articles/hdinsight/hdinsight-machine-learning-overview.md

@@ -125,4 +125,4 @@ The `model` object can now be used to make predictions. For the  full sample of
 
 ## See also
 
-* [Data Science using Scala and Apache Spark on Azure](../../machine-learning/team-data-science-process/scala-walkthrough.md)
+* [Data Science using Scala and Apache Spark on Azure](/azure/architecture/data-science-process/scala-walkthrough)

articles/hdinsight/spark/apache-spark-creating-ml-pipelines.md

@@ -1132,9 +1132,9 @@ The error message from Hive is normally reported back in the Error Log so that y
 
 See the following articles for help with Hive queries for machine learning:
 
-+ [Create Hive tables and load data from Azure Blob Storage](../team-data-science-process/move-hive-tables.md)
-+ [Explore data in tables with Hive queries](../team-data-science-process/explore-data-hive-tables.md)
-+ [Create features for data in an Hadoop cluster using Hive queries](../team-data-science-process/create-features-hive.md)
++ [Create Hive tables and load data from Azure Blob Storage](/azure/architecture/data-science-process/move-hive-tables)
++ [Explore data in tables with Hive queries](/azure/architecture/data-science-process/explore-data-hive-tables)
++ [Create features for data in an Hadoop cluster using Hive queries](/azure/architecture/data-science-process/create-features-hive)
 + [Hive for SQL Users Cheat Sheet (PDF)](http://hortonworks.com/wp-content/uploads/2013/05/hql_cheat_sheet.pdf)
 
 

articles/machine-learning/algorithm-module-reference/designer-error-codes.md

@@ -258,7 +258,7 @@ The data is read as a data frame:
 ### Azure Synapse Analytics and databases
 Azure Synapse Analytics is an elastic data warehouse as a service with an enterprise-class SQL Server experience.
 
-You can provision Azure Synapse Analytics by following the instructions in [this article](../../synapse-analytics/sql-data-warehouse/create-data-warehouse-portal.md). After you provision Azure Synapse Analytics, you can use [this walkthrough](../team-data-science-process/sqldw-walkthrough.md) to do data upload, exploration, and modeling by using data within Azure Synapse Analytics.
+You can provision Azure Synapse Analytics by following the instructions in [this article](../../synapse-analytics/sql-data-warehouse/create-data-warehouse-portal.md). After you provision Azure Synapse Analytics, you can use [this walkthrough](/azure/architecture/data-science-process/sqldw-walkthrough) to do data upload, exploration, and modeling by using data within Azure Synapse Analytics.
 
 #### Azure Cosmos DB
 Azure Cosmos DB is a NoSQL database in the cloud. You can use it to work with documents like JSON, and to store and query the documents.

articles/machine-learning/data-science-virtual-machine/vm-do-ten-things.md

@@ -0,0 +1,44 @@
+### YamlMime:Landing
+
+title: Team Data Science Process Documentation
+summary: Learn how to use the Team Data Science Process, an agile, iterative data science methodology for predictive analytics solutions and intelligent applications.
+metadata:
+  title: Team Data Science Process Documentation
+  description:  Learn how to use the Team Data Science Process, an agile, iterative data science methodology for predictive analytics solutions and intelligent applications.
+  ms.service: machine-learning
+  ms.subservice: team-data-science-process
+  ms.topic: landing-page
+  author: marktab
+  ms.author: marktab
+  ms.date: 03/10/2020
+
+# linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | sample | tutorial | video | whats-new
+
+landingContent:
+# Cards and links should be based on top customer tasks or top subjects
+# Start card title with a verb
+  # Card (optional)
+  - title: Learn about the process
+    linkLists:
+      - linkListType: overview
+        links:
+          - text: What is the Team Data Science Process?
+            url: /azure/architecture/overview
+      - linkListType: concept
+        links:
+          - text: Process lifecycle
+            url: /azure/architecture/lifecycle
+          - text: Business understanding
+            url: /azure/architecture/lifecycle-business-understanding
+          - text: Data acquisition
+            url: /azure/architecture/lifecycle-data
+
+  # Card (optional)
+  - title: Plan & develop a project
+    linkLists:
+      - linkListType: concept
+        links:
+          - text: Project planning
+            url: /azure/architecture/team-data-science-process-project-templates
+          - text: Agile development
+            url: /azure/architecture/agile-development

articles/machine-learning/team-data-science-process/index.yml

@@ -194,7 +194,7 @@ After the indexer is run, you can read the projected data in the containers or t
 
 For analytics, exploration in Power BI is as simple as setting Azure Table Storage as the data source. You can easily create a set of visualizations on your data using the relationships within.
 
-Alternatively, if you need to use the enriched data in a data science pipeline, you could [load the data from blobs into a Pandas DataFrame](../machine-learning/team-data-science-process/explore-data-blob.md).
+Alternatively, if you need to use the enriched data in a data science pipeline, you could [load the data from blobs into a Pandas DataFrame](/azure/architecture/data-science-process/explore-data-blob).
 
 Finally, if you need to export your data from the knowledge store, Azure Data Factory has connectors to export the data and land it in the database of your choice. 
 

articles/search/knowledge-store-projection-overview.md

@@ -284,7 +284,7 @@ Configuring WAF at your application gateway provides the following benefits:
 
 The Anomaly Detection API is an API that's useful for detecting a variety of anomalous patterns in your time series data. The API assigns an anomaly score to each data point in the time series, which can be used for generating alerts, monitoring through dashboards, or connecting with your ticketing systems.
 
-The [Anomaly Detection API](../../machine-learning/team-data-science-process/apps-anomaly-detection-api.md) can detect the following types of anomalies on time series data:
+The [Anomaly Detection API](/azure/architecture/data-science-process/apps-anomaly-detection-api) can detect the following types of anomalies on time series data:
 
 -	**Spikes and dips**: When you're monitoring the number of login failures to a service or number of checkouts in an e-commerce site, unusual spikes or dips could indicate security attacks or service disruptions.
 

articles/security/fundamentals/threat-detection.md

@@ -59,5 +59,5 @@ The following table summarizes the differences in key capabilities.
 
 ## Next steps
 
-- Learn how to [transfer data with Azure Storage Explorer](../../machine-learning/team-data-science-process/move-data-to-azure-blob-using-azure-storage-explorer.md).
+- Learn how to [transfer data with Azure Storage Explorer](/azure/architecture/data-science-process/move-data-to-azure-blob-using-azure-storage-explorer).
 - [Transfer data with AzCopy](./storage-use-azcopy-v10.md)