Merge pull request #213186 from sdgilley/sdg-main-toc

move v1 articles to v1 TOC

Author: BCS2022

articles/machine-learning/toc.yml

@@ -299,8 +299,6 @@
           href: how-to-manage-rest.md
         - name: How to move a workspace
           href: how-to-move-workspace.md
-        - name: Link to Azure Synapse Analytics workspace
-          href: how-to-link-synapse-ml-workspaces.md
         - name: Securely integrate Azure Synapse & Azure Machine Learning
           href: how-to-private-endpoint-integration-synapse.md
         - name: Workspace Diagnostics
@@ -333,7 +331,7 @@
           displayName: compute target, instance types, AKS, Arc Kubernetes
           href: how-to-manage-kubernetes-instance-types.md
         - name: Manage AzureML inference router
-          displayNmae: AKS, Arc Kubernetes, inference, compute target
+          displayName: AKS, Arc Kubernetes, inference, compute target
           href: how-to-kubernetes-inference-routing-azureml-fe.md
         - name: Secure inferencing environment
           displayName: AKS, Arc Kubernetes, HTTPS, private IP, no-public IP, private link, private endpoint, inference
@@ -378,9 +376,7 @@
         - name: Set up software environments
           displayName: pip, Conda, anaconda
           href: how-to-use-environments.md
-        - name: Set input & output directories
-          displayName: large data, write, experiment files, size limit
-          href: how-to-save-write-experiment-files.md
+
         - name: Set up VS Code extension
           displayName: Visual Studio Code, VSCode, debug, bugs, remote, compute, instance
           href: how-to-setup-vs-code.md
@@ -488,9 +484,6 @@
             - name: Use automated ML with Databricks
               displayName: automl
               href: how-to-configure-databricks-automl-environment.md
-            - name: Auto-train a forecast model
-              displayName: time series
-              href: how-to-auto-train-forecast.md
             - name: Prep image data for computer vision models (Python)
               displayName: SDK, automl, image, datasets, conversion scripts, schema, image model
               href: how-to-prepare-datasets-for-automl-images.md
@@ -503,12 +496,6 @@
             - name: Auto-train a natural language processing model
               displayName: nlp, auto ML, automl, SDK
               href: how-to-auto-train-nlp-models.md
-            - name: Data splits & cross-validation (Python)
-              displayName: automl, feature engineering, feature importance
-              href: how-to-configure-cross-validation-data-splits.md
-            - name: Featurization in automated ML (Python)
-              displayName: automl, feature engineering, feature importance, BERT
-              href: how-to-configure-auto-features.md
             - name: Understand charts and metrics
               href: how-to-understand-automated-ml.md
             - name: Generate AutoML training code
@@ -518,8 +505,6 @@
             - name: Inference image models with ONNX model
               displayName: automl, image, image model, computer vision
               href: how-to-inference-onnx-automl-image-models.md
-            - name: Troubleshoot automated ML
-              href: how-to-troubleshoot-auto-ml.md
     - name: Deploy models
       items: 
         - name: Online endpoints (real-time)

articles/machine-learning/v1/toc.yml

@@ -7,7 +7,6 @@
   - name: Architecture & terms 
     displayName: architecture, concepts, definitions, glossary
     href: concept-azure-machine-learning-architecture.md
-
 - name: Upgrade to SDK v2
   items:
     - name: Upgrade overview
@@ -53,39 +52,10 @@
     - name: "Migrate endpoints"
       displayName: migration, v1, v2
       href: ../migrate-to-v2-deploy-endpoints.md  
-
-- name: Concepts (v1)
-  items:
-  - name: Model training
-    displayName: run config, machine learning pipeline, ml pipeline, train model
-    href: concept-train-machine-learning-model-v1.md
-  - name: Work with data
-    items:
-    - name: Data access
-      href: concept-data.md
-    - name: Studio network data access
-      href: concept-network-data-access.md
-  - name: Automated ML overview
-    displayName: automl, auto ml
-    href: concept-automated-ml-v1.md
-  - name: Manage the ML lifecycle (MLOps)
-    items:
-    - name: MLOps capabilities
-      displayName: deploy, deployment, publish, production, operationalize, operationalization
-      href: concept-model-management-and-deployment.md
-    - name: MLflow
-      href: concept-mlflow-v1.md
-    - name: Manage resources VS Code
-      displayName: vscode,resources
-      href: ../how-to-manage-resources-vscode.md
-    - name: Git integration
-      displayName: github gitlab
-      href: ../concept-train-model-git-integration.md
 - name: Tutorials (v1)
   expanded: true
   items:
     - name: Python get started (Day 1)
-      expanded: true
       items:
         - name: 1. Run a Python script
           href: tutorial-1st-experiment-hello-world.md
@@ -112,22 +82,42 @@
     - name: Examples repository
       displayName: example, examples, jupyter, python, notebook, github
       href: https://github.com/azure/machinelearningnotebooks
-- name: How-to guides (v1)
+- name: Concepts (v1)
   items:
-  - name: Install and set up the CLI (v1)
-    displayName: azurecli, mlops
-    href: reference-azure-machine-learning-cli.md
-  - name: Manage workspace using CLI (v1)
-    href: how-to-manage-workspace-cli.md
-  - name: Set up software environments CLI (v1)
-    href: how-to-use-environments.md
-  - name: Use private Python packages
-    displayName: pip, Conda, anaconda
-    href: how-to-use-private-python-packages.md
+  - name: Model training
+    displayName: run config, machine learning pipeline, ml pipeline, train model
+    href: concept-train-machine-learning-model-v1.md
+  - name: Work with data
+    items:
+    - name: Data access
+      href: concept-data.md
+    - name: Studio network data access
+      href: concept-network-data-access.md
+  - name: Automated ML overview
+    displayName: automl, auto ml
+    href: concept-automated-ml-v1.md
+  - name: Manage the ML lifecycle (MLOps)
+    items:
+    - name: MLOps capabilities
+      displayName: deploy, deployment, publish, production, operationalize, operationalization
+      href: concept-model-management-and-deployment.md
+    - name: MLflow
+      href: concept-mlflow-v1.md
+    - name: Manage resources VS Code
+      displayName: vscode,resources
+      href: ../how-to-manage-resources-vscode.md
+    - name: Git integration
+      displayName: github gitlab
+      href: ../concept-train-model-git-integration.md
+- name: Infrastructure & security (v1)
+  items:
+
   - name: Create & manage compute resources
-  - name: Workspace Diagnostics
-    href: how-to-workspace-diagnostic-api.md
     items:
+    - name: Manage workspace using CLI (v1)
+      href: how-to-manage-workspace-cli.md
+    - name: Workspace Diagnostics
+      href: how-to-workspace-diagnostic-api.md
     - name: Compute instance
       displayName: compute target
       href: how-to-create-manage-compute-instance.md
@@ -137,6 +127,8 @@
     - name: Azure Kubernetes Service
       displayName: AKS, inference
       href: how-to-create-attach-kubernetes.md
+    - name: Link to Azure Synapse Analytics workspace
+      href: ../how-to-link-synapse-ml-workspaces.md
   - name: Security
     items:
     - name: Use managed identities for access control
@@ -158,6 +150,20 @@
       - name: Configure secure web services (v1)
         displayName: ssl, tls
         href: how-to-secure-web-service.md
+- name: How-to guides (v1)
+  items:
+  - name: Install and set up the CLI (v1)
+    displayName: azurecli, mlops
+    href: reference-azure-machine-learning-cli.md
+  - name: Set up software environments CLI (v1)
+    href: how-to-use-environments.md
+  - name: Set input & output directories
+    displayName: large data, write, experiment files, size limit
+    href: ../how-to-save-write-experiment-files.md
+  - name: Use private Python packages
+    displayName: pip, Conda, anaconda
+    href: how-to-use-private-python-packages.md
+
   - name: Work with data
     items:
     - name: Access data
@@ -247,15 +253,26 @@
       - name: Auto-train a natural language processing model
         displayName: nlp, auto ML, automl, SDK
         href: how-to-auto-train-nlp-models-v1.md
+      - name: Auto-train a forecast model
+        displayName: time series
+        href: ../how-to-auto-train-forecast.md
       - name: Set up AutoML to train computer vision models with Python
         displayName: auto ML, automl, SDK
         href: how-to-auto-train-image-models-v1.md
       - name: Auto-train a small object detection model
         displayName: computer vision, image, image model
         href: how-to-use-automl-small-object-detect-v1.md
+      - name: Data splits & cross-validation (Python)
+        displayName: automl, feature engineering, feature importance
+        href: ../how-to-configure-cross-validation-data-splits.md
+      - name: Featurization in automated ML (Python)
+        displayName: automl, feature engineering, feature importance, BERT
+        href: ../how-to-configure-auto-features.md
       - name: Local inference using ONNX
         displayName: SDK, automl
         href: how-to-inference-onnx-automl-image-models-v1.md
+      - name: Troubleshoot automated ML
+        href: ../how-to-troubleshoot-auto-ml.md
     - name: Track experiments with MLflow
       displayName: log, monitor, metrics, model registry, register
       href: how-to-use-mlflow.md