toc.yml

- name: Azure Machine Learning Documentation
  href: index.yml
- name: Switch to SDK & CLI v1 documentation
  href: v1/introduction.md
- name: Overview
  items: 
    - name: What is Azure Machine Learning?
      displayName: AML, services, overview, introduction
      href: overview-what-is-azure-machine-learning.md
    - name: What is Azure Machine Learning studio?
      displayName: AML, studio, designer
      href: overview-what-is-machine-learning-studio.md
    - name: How it works
      displayName: concepts, definitions, glossary
      href: concept-azure-machine-learning-v2.md
    
- name: Quickstart
  expanded: true
  items: 
    - name: Create ML resources to get started
      href: quickstart-create-resources.md
- name: Tutorials
  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
        - name: 2. Train your model
          href: tutorial-1st-experiment-sdk-train.md
        - name: 3. Use your own data
          href: tutorial-1st-experiment-bring-data.md
    - name: Jupyter Notebooks
      items: 
        - name: Train & deploy image classification
          displayName: MNIST
          href: tutorial-train-deploy-notebook.md
        - name: Automated ML
          items: 
            - name: Regression (NYC Taxi data)
              displayName: automl, automated, auto ml,
              href: tutorial-auto-train-models.md
            - name: Object detection with AutoML
              displayName: automl, automated, auto ml, computer vision, images, image model
              href: tutorial-auto-train-image-models.md
        - name: Prep your code for production
          displayName: mlops, mlopspython, production
          href: tutorial-convert-ml-experiment-to-production.md
    - name: Studio
      items: 
        - name: Automated ML (UI)
          items: 
            - name: Create automated ML experiments
              displayName: automl, automated, auto ml, portal, ui
              href: tutorial-first-experiment-automated-ml.md
            - name: Forecast demand (Bike share data)
              displayName: automl, automated, auto ml, portal, ui
              href: tutorial-automated-ml-forecast.md
        - name: Designer (drag-n-drop)
          items: 
            - name: 1. Train a regression model
              displayName: studio
              href: tutorial-designer-automobile-price-train-score.md
            - name: 2. Deploy that model
              displayName: studio
              href: tutorial-designer-automobile-price-deploy.md
    - name: Visual Studio Code
      items: 
        - name: Train a TensorFlow image classification model
          href: tutorial-train-deploy-image-classification-model-vscode.md
    - name: Microsoft Power BI integration
      items: 
        - name: "Part 1: Train and deploy models"
          items: 
            - name: Use Notebooks (code)
              href: tutorial-power-bi-custom-model.md
            - name: Use designer
              href: tutorial-power-bi-designer-model.md
            - name: Use automated ML
              href: tutorial-power-bi-automated-model.md
        - name: "Part 2: Consume in Power BI"
          href: /power-bi/connect-data/service-aml-integrate?context=azure/machine-learning/context/ml-context
    - name: Create a secure workspace
      href: tutorial-create-secure-workspace.md
    - name: Create a secure workspace with templates
      href: tutorial-create-secure-workspace-template.md
    - name: Build a training pipeline (Python SDK V2)(preview)
      href: tutorial-pipeline-python-sdk.md
- name: Samples
  items: 
    - name: Jupyter Notebooks
      displayName: example, examples, server, jupyter, azure notebooks, python, notebook, github
      href: samples-notebooks.md
    - name: Examples repository
      displayName: example, examples, jupyter, python, notebook, github
      href: https://github.com/Azure/azureml-examples
    - name: Designer examples & datasets
      displayName: example, examples, studio, pipelines, data sets, modules, components
      href: samples-designer.md
    - name: End-to-end MLOps examples
      href: https://github.com/microsoft/MLOps
- name: Concepts
  items: 
    - name: What is v2?
      href: concept-v2.md
    - name: Train models
      items: 
        - name: Model training
          displayName: run config, machine learning pipeline, ml pipeline, train model
          href: concept-train-machine-learning-model.md
        - name: Distributed training
          displayName: parallellization, deep learning, deep neural network, dnn
          href: concept-distributed-training.md
        - name: Deep learning
          displayName: deep learning vs machine learning, deep learning, vs, versus
          href: concept-deep-learning-vs-machine-learning.md
        - name: AutoML
          items: 
            - name: Automated ML overview
              displayName: automl, auto ml
              href: concept-automated-ml.md
            - name: Overfitting & imbalanced data
              displayName: automl, auto ml, risks
              href: concept-manage-ml-pitfalls.md
        - name: Designer (drag-n-drop ML)
          items: 
            - name: Designer overview
              displayName: studio
              href: concept-designer.md
            - name: Algorithm cheat sheet
              href: algorithm-cheat-sheet.md
            - name: How to select algorithms
              href: how-to-select-algorithms.md
    - name: Deploy models
      items: 
        - name: Endpoints
          href: concept-endpoints.md
        - name: Model portability (ONNX)
          href: concept-onnx.md
        - name: Prebuilt docker images for inference
          href: concept-prebuilt-docker-images-inference.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: Open-source integrations
          href: concept-open-source.md
        - name: ML pipelines
          href: concept-ml-pipelines.md
        - name: ML components
          href: concept-component.md
        - name: MLflow
          href: concept-mlflow.md
    - name: Responsible AI
      items:
        - name: Responsible AI overview
          displayName: responsible, ml
          href: concept-responsible-ml.md
        - name: Implement Responsible AI in Practice
          items:
           - name: Responsible AI dashboard
             href: concept-responsible-ai-dashboard.md
           - name: Model interpretability
             displayName: explainability, explanations, interpretability
             href: how-to-machine-learning-interpretability.md
           - name: Fairness in Machine Learning
             displayName: fairness, bias, model performance, disparity metrics, unfairness
             href: concept-fairness-ml.md
           - name: Causal analysis
             displayName: causal analysis, causal inference
             href: concept-causal-inference.md
           - name: Assess errors in ML models
             displayName: error analysis, model error
             href: concept-error-analysis.md
           - name: Understand your datasets
             displayName: exploratory data analysis, EDA, dataset explorer
             href: concept-data-analysis.md
           - name: Counterfactual analysis and what-if
             href: concept-counterfactual-analysis.md
           - name: Differential Privacy
             displayName: differential,privacy
             href: concept-differential-privacy.md
    - name: Workspace & compute resources
      items: 
        - name: Workspace
          href: concept-workspace.md
        - name: Environments
          href: concept-environments.md
        - name: Compute instance
          displayName: resource, dsvm, Data Science Virtual Machine
          href: concept-compute-instance.md
        - name: Compute target
          displayName: resource, dsvm, AKS, kubernetes, amlcompute, Data Science Virtual Machine, local, cluster, ACI, container instance, ADB, Databricks, data lake, lake, HDI, HDInsight
          href: concept-compute-target.md
        - name: Plan and manage costs
          displayName: low priority VM
          href: concept-plan-manage-cost.md
    - name: Work with data
      items: 
        - name: Datastores
          href: concept-datastore.md
        - name: Access data
          href: concept-data.md
    - name: Collect data
      items:
        - name: Sourcing human data responsibly
          href: concept-sourcing-human-data.md
    - name: Security
      items: 
        - name: Enterprise security overview
          displayName: authorization authentication encryption aad azure active directory
          href: concept-enterprise-security.md
        - name: Secured workspace traffic flow
          href: concept-secure-network-traffic-flow.md
        - name: Security baseline
          href: /security/benchmark/azure/baselines/machine-learning-security-baseline?context=/azure/machine-learning/context/ml-context
        - name: Security controls by Azure Policy
          href: ./security-controls-policy.md
        - name: Data encryption
          href: concept-data-encryption.md
        - name: Customer-managed keys
          href: concept-customer-managed-keys.md
        - name: Vulnerability management
          href: concept-vulnerability-management.md
- name: How-to guides
  items: 
    - name: Security
      items: 
        - name: Identity & access management
          items: 
            - name: Set up authentication
              displayName: authentication, auth, oauth
              href: how-to-setup-authentication.md
            - name: Manage users and roles
              href: how-to-assign-roles.md
            - name: Use managed identities for access control
              href: how-to-use-managed-identities.md
            - name: Use Azure AD identity in AKS deployments
              href: how-to-use-azure-ad-identity.md
        - name: Network security
          items: 
            - name: Virtual network overview
              displayName: vnet, blob
              href: how-to-network-security-overview.md
            - name: Secure workspace resources
              displayName: vnet, workspace, container instance, container registry
              href: how-to-secure-workspace-vnet.md
            - name: Secure training environment
              displayName: vnet, No public IP
              href: how-to-secure-training-vnet.md
            - name: Use studio in a virtual network
              displayName: vnet
              href: how-to-enable-studio-virtual-network.md
            - name: Use private endpoint
              displayName: private endpoint
              href: how-to-configure-private-link.md
            - name: Use custom DNS
              href: how-to-custom-dns.md
            - name: Configure required network traffic
              displayName: firewall, user-defined route, udr
              href: how-to-access-azureml-behind-firewall.md
            - name: Configure network isolation with v2
              href: how-to-configure-network-isolation-with-v2.md
        - name: Data protection
          items: 
            - name: Failover & disaster recovery
              displayName: high availability
              href: how-to-high-availability-machine-learning.md
            - name: Regenerate storage access keys
              href: how-to-change-storage-access-key.md
        - name: Monitor Azure Machine Learning
          href: monitor-azure-machine-learning.md
        - name: Secure coding
          displayName: security threat
          href: concept-secure-code-best-practice.md
        - name: Audit and manage
          displayName: policy
          href: how-to-integrate-azure-policy.md
    - name: Workspaces & compute resources
      items:
          - name: Create & manage workspaces
            items:
              - name: Use Azure portal or Python SDK
                href: how-to-manage-workspace.md
              - name: Use Azure CLI
                href: how-to-manage-workspace-cli.md
              - name: Use Resource Manager template
                displayName: arm
                href: how-to-create-workspace-template.md
              - name: Use Terraform
                href: how-to-manage-workspace-terraform.md
              - name: Use REST
                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
                href: how-to-workspace-diagnostic-api.md
          - name: Create & manage compute resources
            items:
              - name: Compute instance
                displayName: compute target, No public IP
                href: how-to-create-manage-compute-instance.md
              - name: Compute cluster
                displayName: compute target, low priority, managed identity
                href: how-to-create-attach-compute-cluster.md
              - name: Kubernetes cluster
                displayName: Azure Arc, Kubernetes, on-premise, multi-cloud
                href: how-to-attach-kubernetes-anywhere.md
              - name: Use studio
                displayName: compute target, dsvm, Data Science Virtual Machine, local, cluster, ACI, container instance, Databricks, data lake, lake, HDI, HDInsight, low priority, managed identity
                href: how-to-create-attach-compute-studio.md
          - name: Customer-managed keys
            href: how-to-setup-customer-managed-keys.md
    - name: Set up an environment
      items: 
        - name: Set up dev environments
          displayName: local, remote, azure notebooks, notebook, Jupyter, dsvm, Databricks, ADB
          href: how-to-configure-environment.md
        - name: Install and set up the CLI (v2)
          displayName: azurecli, mlops
          href: how-to-configure-cli.md
        - name: Set up studio environment
          items: 
            - name: Manage environments in studio
              displayName: environments, studio, create, edit
              href: how-to-manage-environments-in-studio.md
            - name: Create and manage files
              displayName: Jupyter, notebooks, clone, samples
              href: how-to-manage-files.md
            - name: Run Jupyter Notebooks
              href: how-to-run-jupyter-notebooks.md
            - name: Use a terminal
              displayName: git, github, clone, kernel, package
              href: how-to-access-terminal.md
        - name: Set up software environments
          displayName: pip, Conda, anaconda
          href: how-to-use-environments.md
        - name: Use private Python packages
          displayName: pip, Conda, anaconda
          href: how-to-use-private-python-packages.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
        - name: Connect to compute instance VS Code
          displayName: Visual Studio Code, VSCode, debug, bugs
          href: how-to-set-up-vs-code-remote.md
        - name: Git integration
          displayName: github gitlab
          href: concept-train-model-git-integration.md
        - name: How to troubleshoot environments
          displayName: troubleshoot, environment, debug, image
          href: how-to-troubleshoot-environments.md
    - name: Work with data
      items: 
        - name: Create a datastore for data storage services
          displayName: datastore, data storage
          href: how-to-datastore.md
        - name: Register a data asset
          displayName: data, data asset
          href: how-to-create-register-data-assets.md
        - name: Reading & writing data
          displayName: read and write data
          href: how-to-read-write-data-v2.md
        - name: Administrate data authentication
          displayName: data authentication
          href: how-to-administrate-data-authentication.md
        - name: Label data
          items: 
          - name: Set up image labeling
            displayName: data, dataset
            href: how-to-create-image-labeling-projects.md
          - name: Set up text labeling
            displayName: data, dataset
            href: how-to-create-text-labeling-projects.md
          - name: Label images and text
            displayName: data, dataset, labeling
            href: how-to-label-data.md
          - name: Add users
            displayName: data, dataset, labeling
            href: how-to-add-users.md
          - name: Outsource labeling tasks
            href: how-to-outsource-data-labeling.md
    - name: Train models
      items: 
        - name: Train with the job creation UI
          href: how-to-train-with-ui.md
        - name: Train with the Python SDK
          items:
          - name: Train with SDK v2 (preview)
            href: how-to-train-sdk.md
          - name: Configure & submit training run
            displayName: run config, script run config, scriptrunconfig, compute target, dsvm, Data Science Virtual Machine, local, cluster, ACI, container instance, Databricks, data lake, lake, HDI, HDInsight
            href: how-to-set-up-training-targets.md
          - name: Tune hyperparameters
            displayName: parameter, hyperparameter, hyperdrive
            href: how-to-tune-hyperparameters.md
          - name: Distributed GPU guide
            displayName: MPI, Horovod, DeepSpeed, PyTorch, Hugging Face Transformers, TensorFlow, InfiniBand
            href: how-to-train-distributed-gpu.md
          - name: Scikit-learn
            href: how-to-train-scikit-learn.md
          - name: TensorFlow
            href: how-to-train-tensorflow.md
          - name: Keras
            href: how-to-train-keras.md
          - name: PyTorch
            href: how-to-train-pytorch.md
          - name: Train with custom Docker image
            href: how-to-train-with-custom-image.md
          - name: Migrate from Estimators to ScriptRunConfig
            href: how-to-migrate-from-estimators-to-scriptrunconfig.md
          - name: Reinforcement learning
            href: how-to-use-reinforcement-learning.md
          - name: Train experiments with MLflow Projects
            displayName: log, monitor, metrics, model registry, register
            href: how-to-train-mlflow-projects.md
          - name: Track & monitor training
            items:
              - name: Track, monitor, and analyze runs
                displayName: cancel, fail, status, child run
                href: how-to-track-monitor-analyze-runs.md
              - name: Track experiments with MLflow and CLI (v2)
                displayName: log, monitor, metrics, model registry, register
                href: how-to-use-mlflow-cli-runs.md
              - name: Track Azure Databricks runs with MLflow
                displayName: log, monitor, deploy, metrics, databricks
                href: how-to-use-mlflow-azure-databricks.md
              - name: Log & view metrics and log files
                displayName: troubleshoot, log, files, tracing, metrics
                href: how-to-log-view-metrics.md
              - name: Visualize runs with TensorBoard
                displayName: log, monitor, metrics
                href: how-to-monitor-tensorboard.md
              - name: Migrate from SDK v1 logging to SDK v2
                href: reference-migrate-sdk-v1-mlflow-tracking.md
          - name: Use Key Vault when training
            displayName: secrets keyvault
            href: how-to-use-secrets-in-runs.md
        - name: Train with the CLI v2
          href: how-to-train-cli.md
        - name: Train with the REST API
          href: how-to-train-with-rest.md
        - name: Manage environments with the CLI (v2)
          displayName: environment
          href: how-to-manage-environments-v2.md
        - name: Automated machine learning
          displayName: automl, auto ml
          items: 
            - name: Use automated ML (Python)
              displayName: SDK, automl
              href: how-to-configure-auto-train.md
            - name: Use automated ML (interface)
              href: how-to-use-automated-ml-for-ml-models.md
            - 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
            - name: Auto-train computer vision models (Python)
              displayName: SDK, automl, image, image model
              href: how-to-auto-train-image-models.md
            - name: Auto-train a small object detection model
              displayName: computer vision, image, image model
              href: how-to-use-automl-small-object-detect.md
            - 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
              href: how-to-generate-automl-training-code.md
            - name: Use ONNX model in .NET application
              href: how-to-use-automl-onnx-model-dotnet.md
            - name: Inference image models with ONNX model
              displayName: automl, image, image model, computer vision
              href: how-to-inference-onnx-automl-image-models.md
            - name: Explain automated ML models
              displayName: SDK, interpret, interpret, explain, explainability, interpretability, automated ML, automl, auto ml
              href: how-to-machine-learning-interpretability-automl.md
            - name: Troubleshoot automated ML
              href: how-to-troubleshoot-auto-ml.md
    - name: Deploy models
      items: 
        - name: Online endpoints (real-time)
          items: 
            - name: Deploy an ML model with an online endpoint (CLI)
              href: how-to-deploy-managed-online-endpoints.md
            - name: Deploy an ML model with an online endpoint (SDK preview)
              href: how-to-deploy-managed-online-endpoint-sdk-v2.md
            - name: Safe rollout for online endpoints (CLI)
              href: how-to-safely-rollout-managed-endpoints.md
            - name: Safe rollout for online endpoints (SDK preview)
              href: how-to-safely-rollout-managed-endpoints-sdk-v2.md
            - name: Deployment scenarios
              items: 
                - name: Deploy a MLflow model with an online endpoint
                  displayName: ncd, no code deploy, no-code deployment
                  href: how-to-deploy-mlflow-models-online-endpoints.md
                - name: Deploy a custom container with an online endpoint
                  href: how-to-deploy-custom-container.md
                - name: Use online endpoints in studio
                  href: how-to-use-managed-online-endpoint-studio.md
                - name: High-performance serving with Triton
                  href: how-to-deploy-with-triton.md
                - name: Use REST to deploy a model as an online endpoint
                  href: how-to-deploy-with-rest.md
                - name: Deploy an AutoML model with an online endpoint
                  href: how-to-deploy-automl-endpoint.md            
            - name: Security
              items:
                - name: Authenticate to endpoints
                  href: how-to-authenticate-online-endpoint.md
                - name: Network isolation with managed online endpoints
                  displayName: network, vnet, secure
                  href: how-to-secure-online-endpoint.md
                - name: Access Azure resources from online endpoints
                  href: how-to-access-resources-from-endpoints-managed-identities.md
            - name: Autoscale online endpoints
              href: how-to-autoscale-endpoints.md
            - name: Managed online endpoints VM SKU list
              href: reference-managed-online-endpoints-vm-sku-list.md
            - name: Viewing managed online endpoint costs
              href: how-to-view-online-endpoints-costs.md
            - name: Monitoring online endpoints
              href: how-to-monitor-online-endpoints.md
            - name: Debug online endpoints locally VS Code
              href: how-to-debug-managed-online-endpoints-visual-studio-code.md
            - name: Troubleshoot online endpoints
              href: how-to-troubleshoot-online-endpoints.md
        - name: Batch endpoints
          items: 
            - name: Batch scoring with batch endpoints (CLI)
              href: how-to-use-batch-endpoint.md
            - name: Batch scoring with batch endpoints (SDK preview)
              href: how-to-use-batch-endpoint-sdk-v2.md
            - name: Batch endpoints in studio
              href: how-to-use-batch-endpoints-studio.md
            - name: Use REST to deploy a model as a batch endpoint
              href: how-to-deploy-batch-with-rest.md
            - name: Troubleshoot batch endpoints
              href: how-to-troubleshoot-batch-endpoints.md
        - name: Inference HTTP server
          displayName: local debug
          href: how-to-inference-server-http.md
        - name: Convert custom model to MLflow model
          displayName: ncd, no code deploy, no-code deployment
          href: how-to-convert-custom-model-to-mlflow.md
    - name: Manage the ML lifecycle
      items: 
        - name: Manage models
          href: how-to-manage-models.md
        - name: Automation
          items: 
            - name: Azure Pipelines for CI/CD
              displayName: continuous, integration, delivery
              href: /azure/devops/pipelines/targets/azure-machine-learning?context=azure/machine-learning/context/ml-context
            - name: GitHub Actions for CI/CD
              href: how-to-github-actions-machine-learning.md
            - name: Create event-driven workflows
              displayName: event grid
              href: how-to-use-event-grid.md
        - name: Build & use ML pipelines
          displayName: pipelines
          items:
            - name: Create ML pipelines using components (CLI v2)
              href: how-to-create-component-pipelines-cli.md
            - name: Create ML pipelines using components (Python SDK v2)
              href: how-to-create-component-pipeline-python.md
            - name: Create ML pipelines using components (UI)
              href: how-to-create-component-pipelines-ui.md
            - name: How to use sweep in pipelines (v2)
              href: how-to-use-sweep-in-pipeline.md
            - name: Build and debug pipelines (UI)
              href: how-to-use-pipeline-ui.md
            - name: Designer (drag-n-drop)
              items: 
                - name: Log metrics
                  displayName: designer, logging
                  href: how-to-track-designer-experiments.md
                - name: Transform data
                  displayName: pipeline
                  href: how-to-designer-transform-data.md
                - name: Use pipeline parameters
                  displayName: designer, pipeline parameters
                  href: how-to-use-pipeline-parameter.md
                - name: Retrain using published pipelines
                  displayName: retrain, designer, published pipeline
                  href: how-to-retrain-designer.md
                - name: Batch predictions
                  displayName: score scoring asynchronous consume pipeline parallelrunstep inference designer
                  href: how-to-run-batch-predictions-designer.md
                - name: Execute Python code
                  displayName: feature extraction, feature engineering
                  href: how-to-designer-python.md
        - name: Manage resource quotas
          displayName: limits
          href: how-to-manage-quotas.md
        - name: Manage and optimize cost
          displayName: cost-management,cost-optimization
          href: how-to-manage-optimize-cost.md
        - name: Manage resources VS Code
          displayName: vscode,resources
          href: how-to-manage-resources-vscode.md
    - name: Implement Responsible AI in Practice
      items:
        - name: Generate Responsible AI dashboard
          items:
            - name: Responsible AI dashboard (V2)
              displayName: explanations, interpretability, fairness, bias, model performance, disparity metrics, unfairness, causal analysis, causal inference, exploratory data analysis, EDA, dataset explorer
              href: how-to-responsible-ai-dashboard-sdk-cli.md
            - name: Responsible AI dashboard (UI)
              displayName: explanations, interpretability, fairness, bias, model performance, disparity metrics, unfairness, causal analysis, causal inference, exploratory data analysis, EDA, dataset explorer
              href: how-to-responsible-ai-dashboard-ui.md
        - name: Using the Responsible AI dashboard in studio
          displayName: explanations, interpretability, fairness, bias, model performance, disparity metrics, unfairness, causal analysis, causal inference, exploratory data analysis, EDA, dataset explorer
          href: how-to-responsible-ai-dashboard.md
        - name: Responsible AI dashboard scorecard
          displayName: explanations, interpretability, fairness, bias, model performance, disparity metrics, unfairness, causal analysis, causal inference, exploratory data analysis, EDA, dataset explorer
          href: how-to-responsible-ai-scorecard.md
- name: Troubleshoot & debug
  items: 
    - name: VS Code interactive debugging
      displayName: vscode,remote,debug,pipelines,deployments,ssh
      href: how-to-debug-visual-studio-code.md
- name: Reference
  items: 
    - name: Python SDK
      displayName: api, core, package, python, class
      href: /python/api/overview/azure/ml/intro
    - name: REST API
      href: /rest/api/azureml/
    - name: CLI (v2)
      displayName: extension, command line, command-line, az, ci/cd, automate, automated
      href: /cli/azure/ml/
    - name: CLI (v2) YAML schemas
      items: 
        - name: Overview
          href: reference-yaml-overview.md
        - name: Core syntax
          href: reference-yaml-core-syntax.md
        - name: Workspace
          href: reference-yaml-workspace.md
        - name: Environment
          href: reference-yaml-environment.md
        - name: Data
          href: reference-yaml-data.md
        - name: Model
          href: reference-yaml-model.md
        - name: Compute
          items: 
            - name: Compute cluster (AmlCompute)
              href: reference-yaml-compute-aml.md
            - name: Compute instance
              href: reference-yaml-compute-instance.md
            - name: Attached Virtual Machine
              href: reference-yaml-compute-vm.md
            - name: Attached Azure Arc-enabled Kubernetes (KubernetesCompute)
              href: reference-yaml-compute-kubernetes.md
        - name: Job
          items: 
            - name: Command
              href: reference-yaml-job-command.md
            - name: Sweep
              href: reference-yaml-job-sweep.md
            - name: Pipeline
              href: reference-yaml-job-pipeline.md
        - name: Datastore
          items: 
            - name: Azure Blob
              href: reference-yaml-datastore-blob.md
            - name: Azure Files
              href: reference-yaml-datastore-files.md
            - name: Azure Data Lake Gen1
              href: reference-yaml-datastore-data-lake-gen1.md
            - name: Azure Data Lake Gen2
              href: reference-yaml-datastore-data-lake-gen2.md
        - name: Endpoint
          items: 
            - name: Online (real-time)
              href: reference-yaml-endpoint-online.md
            - name: Batch
              href: reference-yaml-endpoint-batch.md
        - name: Deployment
          items: 
            - name: Managed online (real-time)
              href: reference-yaml-deployment-managed-online.md
            - name: Kubernetes online (real-time)
              href: reference-yaml-deployment-kubernetes-online.md
            - name: Batch
              href: reference-yaml-deployment-batch.md
        - name: Component
          items: 
            - name: Command
              href: reference-yaml-component-command.md
    - name: Image data schemas for AutoML
      displayName: automl, schema, data, computer vision, image model
      href: reference-automl-images-schema.md
    - name: Hyperparameters for AutoML computer vision tasks
      displayName: automl, yolo-5, image, image model
      href: reference-automl-images-hyperparameters.md
    - name: Designer component reference
      displayName: module, component, reference, algorithm, studio
      href: algorithm-module-reference/module-reference.md
    - name: Monitor data reference
      href: monitor-resource-reference.md
    - name: Azure Policy built-ins
      displayName: samples, policies, definitions
      href: ./policy-reference.md
    - name: Curated environments
      displayName: environments, curated environments
      href: resource-curated-environments.md
    - name: Azure CLI
      href: /cli/azure/ml
- name: Resources
  items: 
    - name: Python SDK release notes
      displayName: what's, new, issues, bugs
      href: azure-machine-learning-release-notes.md
    - name: CLI (v2) release notes
      displayName: what's, new, issues, bugs
      href: azure-machine-learning-release-notes-cli-v2.md
    - name: Azure roadmap
      href: https://azure.microsoft.com/roadmap/
    - name: Pricing
      href: https://azure.microsoft.com/pricing/details/machine-learning-services/
    - name: Service limits
      href: resource-limits-quotas-capacity.md
    - name: Conda licensing
      href: /legal/machine-learning/conda-licensing
    - name: Regional availability
      href: https://azure.microsoft.com/regions/services/
    - name: Feature availability across regions
      href: reference-machine-learning-cloud-parity.md
    - name: User forum
      href: /answers/topics/azure-machine-learning.html
    - name: Microsoft Learn
      href: /learn/browse/?expanded=azure&products=azure-machine-learning
    - name: Stack Overflow
      href: https://stackoverflow.com/questions/tagged/azure-machine-learning-service
    - name: Compare our ML products
      href: /azure/architecture/data-guide/technology-choices/data-science-and-machine-learning?context=azure/machine-learning/context/ml-context
    - name: What happened to Workbench
      displayName: desktop, ui, Workbench, upgrade, update, transfer
      href: overview-what-happened-to-workbench.md
    - name: Designer accessibility features
      displayName: accessibility, designer, visual interface, screen reader, keyboard shortcuts
      href: designer-accessibility.md