[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EDAPHEC0NJTN9S0AM3YS3R":3,"skill-same:01M6EDAPHEC0NJTN9S0AM3YS3R":56},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":25,"frontMatter":26,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M6EDAPHEC0NJTN9S0AM3YS3R","azure-machine-learning",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FMicrosoftDocs\u002FAgent-Skills\u002Fazure-machine-learning\u002FSKILL.md",[],[18,19,20,21,22],"machine learning","azure","learning","machine","azure machine learning","2026-11-10 10:12:30",false,"# Azure Machine Learning Skill\n\nThis skill provides expert guidance for Azure Machine Learning. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.\n\n## How to Use This Skill\n\n> **IMPORTANT for Agent**: Use the **Category Index** below to locate relevant sections. For categories with line ranges (e.g., `L35-L120`), use `read_file` with the specified lines. For categories with file links (e.g., `[security.md](security.md)`), use `read_file` on the linked reference file\n\n> **IMPORTANT for Agent**: If `metadata.generated_at` is more than 3 months old, suggest the user pull the latest version from the repository. If `mcp_microsoftdocs` tools are not available, suggest the user install it: [Installation Guide](https:\u002F\u002Fgithub.com\u002FMicrosoftDocs\u002Fmcp\u002Fblob\u002Fmain\u002FREADME.md)\n\nThis skill requires **network access** to fetch documentation content:\n- **Preferred**: Use `mcp_microsoftdocs:microsoft_docs_fetch` with query string `from=learn-agent-skill`. Returns Markdown.\n- **Fallback**: Use `fetch_webpage` with query string `from=learn-agent-skill&accept=text\u002Fmarkdown`. Returns Markdown.\n\n## Category Index\n\n| Category | Lines | Description |\n|----------|-------|-------------|\n| Troubleshooting | L37-L71 | Diagnosing and fixing Azure ML runtime issues: pipelines, AutoML, endpoints (online\u002Fbatch), Kubernetes, networking (VNet\u002Fprivate), environments\u002Fimages, prompt flow, and known platform issues. |\n| Best Practices | L72-L95 | Best practices for ML\u002FLLM lifecycle in Azure ML: cost, security, data ethics, feature design, training, deployment, monitoring, AutoML, prompt flow, and performance tuning. |\n| Decision Making | L96-L119 | Guidance on Azure ML design choices: algorithms, training, networking, cost, DR, data labeling, and detailed migration\u002Fupgrade paths from AML v1 to v2 across jobs, data, compute, and workspaces |\n| Architecture & Design Patterns | L120-L127 | Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components. |\n| Limits & Quotas | L128-L136 | Azure ML deployment limits: regional\u002Fsovereign availability, quota management, supported VM SKUs for managed endpoints, and capacity planning against service limits. |\n| Security | L137-L195 | Securing Azure ML workspaces, data, and endpoints: encryption, auth\u002FRBAC, managed identities, network isolation\u002FVNets, private endpoints, policies, key\u002Fsecret management, and compliance controls. |\n| Configuration | L196-L463 | Configuring Azure ML: designer components, AutoML, compute, data, monitoring, registries, prompt flow, and full CLI\u002FYAML schemas for jobs, endpoints, feature stores, and connections. |\n| Integrations & Coding Patterns | L464-L506 | Integrating Azure ML with data sources, Spark\u002FDatabricks\u002FSynapse, REST\u002FMLflow, prompt flow, and external services to move data, run jobs, deploy models, and trigger\u002Fmonitor endpoints. |\n| Deployment | L507-L553 | Deploying and operating ML and LLM workloads in Azure ML: online\u002Fbatch endpoints, MLflow, pipelines, prompt flow, CI\u002FCD, blue‑green rollouts, and cross-workspace\u002Fmodel catalog deployments |\n\n### Troubleshooting\n| Topic | URL |\n|-------|-----|\n| Troubleshoot Azure ML designer component error codes | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fdesigner-error-codes?view=azureml-api-2 |\n| Resolve common Azure AutoML forecasting issues | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-automl-forecasting-faq?view=azureml-api-2 |\n| Debug Azure ML online endpoints locally with VS Code | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-managed-online-endpoints-visual-studio-code?view=azureml-api-2 |\n| Troubleshoot ParallelRunStep failures in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-parallel-run-step?view=azureml-api-1 |\n| Debug Azure ML pipeline failures in studio | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-pipeline-failure?view=azureml-api-2 |\n| Diagnose Azure ML pipeline performance issues with profiling | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-pipeline-performance?view=azureml-api-2 |\n| Debug pipeline reuse behavior in Azure Machine Learning | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-pipeline-reuse-issues?view=azureml-api-2 |\n| Troubleshoot Azure ML SDK v1 pipelines execution | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-debug-pipelines?view=azureml-api-1 |\n| Troubleshoot Azure automated ML experiment failures | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-auto-ml?view=azureml-api-2 |\n| Troubleshoot Azure ML batch endpoints and jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-batch-endpoints?view=azureml-api-2 |\n| Troubleshoot data access issues in Azure ML SDK v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-data-access?view=azureml-api-2 |\n| Troubleshoot Azure ML data labeling project creation | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-data-labeling?view=azureml-api-2 |\n| Troubleshoot Azure ML environment image builds and packages | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-environments?view=azureml-api-2 |\n| Troubleshoot Azure ML Kubernetes compute workloads | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-kubernetes-compute?view=azureml-api-2 |\n| Troubleshoot Azure ML Kubernetes extension deployment | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-kubernetes-extension?view=azureml-api-2 |\n| Troubleshoot Azure ML managed virtual network issues | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-managed-network?view=azureml-api-2 |\n| Diagnose and fix Azure ML online endpoint errors | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-online-endpoints?view=azureml-api-2 |\n| Diagnose and fix Azure ML online endpoint errors | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-online-endpoints?view=azureml-api-2 |\n| Troubleshoot Azure ML online endpoint deployment and scoring errors | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-online-endpoints?view=azureml-api-2 |\n| Troubleshoot Azure ML prebuilt Docker inference images | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-prebuilt-docker-image-inference?view=azureml-api-1 |\n| Resolve 'descriptors cannot be created directly' in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-protobuf-descriptor-error?view=azureml-api-2 |\n| Troubleshoot Azure ML private endpoint connectivity | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-secure-connection-workspace?view=azureml-api-2 |\n| Fix SerializationError import issues in Azure ML SDK v1 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-serialization-error?view=azureml-api-1 |\n| Fix 'Validation for schema failed' errors in Azure ML CLI v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-troubleshoot-validation-for-schema-failed-error?view=azureml-api-2 |\n| Use Azure ML workspace diagnostics for issue analysis | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-workspace-diagnostic-api?view=azureml-api-2 |\n| Review Azure Machine Learning current known issues | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fknown-issues\u002Fazure-machine-learning-known-issues?view=azureml-api-2 |\n| Known issue: Invalid certificate during AKS deployment | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fknown-issues\u002Finferencing-invalid-certificate?view=azureml-api-2 |\n| Known issue: Updating Azure ML Kubernetes compute fails | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fknown-issues\u002Finferencing-updating-kubernetes-compute-appears-to-succeed?view=azureml-api-2 |\n| Troubleshoot Azure ML prompt flow issues | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftroubleshoot-guidance?view=azureml-api-2 |\n| Troubleshoot Azure ML prompt flow issues | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftroubleshoot-guidance?view=azureml-api-2 |\n| Troubleshoot Azure ML managed feature store errors | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ftroubleshooting-managed-feature-store?view=azureml-api-2 |\n\n### Best Practices\n| Topic | URL |\n|-------|-----|\n| Mitigate overfitting and imbalance in Azure AutoML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-manage-ml-pitfalls?view=azureml-api-2 |\n| Understand Azure ML model monitoring concepts and practices | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-model-monitoring?view=azureml-api-2 |\n| Optimize and manage Azure Machine Learning costs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-plan-manage-cost?view=azureml-api-2 |\n| Apply secure coding practices in Azure ML notebooks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-secure-code-best-practice?view=azureml-api-2 |\n| Ethical best practices for sourcing human data | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-sourcing-human-data?view=azureml-api-2 |\n| Design feature set transformations in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ffeature-set-specification-transformation-concepts?view=azureml-api-2 |\n| Author batch scoring scripts for AML batch deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-batch-scoring-script?view=azureml-api-2 |\n| Write advanced Azure ML entry scripts for inference | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-advanced-entry-script?view=azureml-api-1 |\n| Profile AML model CPU and memory usage before deployment | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-profile-model?view=azureml-api-1 |\n| Tune Azure ML Kubernetes inference router performance | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2 |\n| Manage Azure ML compute notebook and terminal sessions | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-compute-sessions?view=azureml-api-2 |\n| Optimize Azure Machine Learning compute costs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-optimize-cost?view=azureml-api-2 |\n| Choose storage locations for Azure ML experiment files | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-save-write-experiment-files?view=azureml-api-1 |\n| Apply best practices for distributed GPU training in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-train-distributed-gpu?view=azureml-api-2 |\n| Evaluate and compare Azure AutoML experiment results | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-understand-automated-ml?view=azureml-api-2 |\n| Optimize AutoML for small object detection in images | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-automl-small-object-detect?view=azureml-api-2 |\n| Apply generative AI monitoring metrics and recommended practices in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fconcept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2 |\n| Design and use evaluation flows and metrics in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-develop-an-evaluation-flow?view=azureml-api-2 |\n| Tune LLM prompts using variants in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-tune-prompts-using-variants?view=azureml-api-2 |\n| Optimize checkpoint performance for large Azure ML models with Nebula | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-checkpoint-performance-for-large-models?view=azureml-api-2 |\n\n### Decision Making\n| Topic | URL |\n|-------|-----|\n| Choose Azure ML designer algorithms with cheat sheet | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Falgorithm-cheat-sheet?view=azureml-api-1 |\n| Plan Azure ML registries for multi-environment MLOps | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-machine-learning-registries-mlops?view=azureml-api-2 |\n| Choose between managed and custom network isolation in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-network-isolation-configurations?view=azureml-api-2 |\n| Choose the right Azure ML training method | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-train-machine-learning-model?view=azureml-api-2 |\n| Choose migration paths from Azure ML Data Import to Fabric | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fdata-import-migration-guide?view=azureml-api-2 |\n| Plan failover and disaster recovery for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-high-availability-machine-learning?view=azureml-api-2 |\n| Decide when and how to upgrade AML v1 to v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-migrate-from-v1?view=azureml-api-2 |\n| Move Azure ML workspaces between subscriptions | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-move-workspace?view=azureml-api-2 |\n| Plan Azure ML network isolation architecture | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-network-isolation-planning?view=azureml-api-2 |\n| Use vendor companies for Azure ML data labeling | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-outsource-data-labeling?view=azureml-api-2 |\n| Select appropriate Azure ML algorithms for tasks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-select-algorithms?view=azureml-api-1 |\n| Use low-priority VMs for AML batch inference cost savings | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-low-priority-batch?view=azureml-api-2 |\n| Map AML v1 datasets to v2 data assets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-assets-data?view=azureml-api-2 |\n| Upgrade model management workflows from AML v1 to v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-assets-model?view=azureml-api-2 |\n| Migrate script run jobs to AML SDK v2 command jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-command-job?view=azureml-api-2 |\n| Upgrade AutoML configurations from AML SDK v1 to v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-execution-automl?view=azureml-api-2 |\n| Compare local run workflows between AML v1 and v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-local-runs?view=azureml-api-2 |\n| Evaluate compute management changes from AML v1 to v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-resource-compute?view=azureml-api-2 |\n| Migrate datastore management from AML v1 to v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-resource-datastore?view=azureml-api-2 |\n| Compare workspace management between AML SDK v1 and v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmigrate-to-v2-resource-workspace?view=azureml-api-2 |\n\n### Architecture & Design Patterns\n| Topic | URL |\n|-------|-----|\n| Plan real-time inference with Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-endpoints-online?view=azureml-api-2 |\n| Understand Azure ML endpoint types for inference | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-endpoints?view=azureml-api-2 |\n| Design data movement patterns in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-move-data-in-out-of-pipelines?view=azureml-api-1 |\n| Design multistep pipeline components in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-pipeline-component?view=azureml-api-2 |\n\n### Limits & Quotas\n| Topic | URL |\n|-------|-----|\n| Check regional availability for Azure ML standard deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-endpoint-serverless-availability?view=azureml-api-2 |\n| Manage Azure ML resource quotas and limits | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-quotas?view=azureml-api-2 |\n| Check Azure ML feature availability by sovereign cloud | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-machine-learning-cloud-parity?view=azureml-api-2 |\n| Supported VM SKUs for Azure ML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-managed-online-endpoints-vm-sku-list?view=azureml-api-2 |\n| Plan capacity with Azure Machine Learning service limits | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fresource-limits-capacity?view=azureml-api-2 |\n\n### Security\n| Topic | URL |\n|-------|-----|\n| Use customer-managed keys with Azure Machine Learning | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-customer-managed-keys?view=azureml-api-2 |\n| Implement data encryption for Azure ML storage and compute | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-data-encryption?view=azureml-api-2 |\n| Understand data handling and privacy for Model Catalog deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-data-privacy?view=azureml-api-2 |\n| Understand auth and RBAC for AML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-endpoints-online-auth?view=azureml-api-2 |\n| Plan enterprise security and governance for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-enterprise-security?view=azureml-api-2 |\n| Secret injection concepts for AML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-secret-injection?view=azureml-api-2 |\n| Understand secure network traffic flow in Azure ML VNets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-secure-network-traffic-flow?view=azureml-api-2 |\n| Network isolation concepts for AML managed endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-secure-online-endpoint?view=azureml-api-2 |\n| Manage vulnerabilities for Azure ML images and components | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-vulnerability-management?view=azureml-api-2 |\n| Configure inbound and outbound network traffic for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-access-azureml-behind-firewall?view=azureml-api-2 |\n| Securely access on-premises resources from Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-access-on-premises-resources?view=azureml-api-2 |\n| Access Azure resources from AML endpoints via managed identity | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-access-resources-from-endpoints-managed-identities?view=azureml-api-2 |\n| Grant limited access to Azure ML labeling projects | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-add-users?view=azureml-api-2 |\n| Administer data access and authentication for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-administrate-data-authentication?view=azureml-api-2 |\n| Configure data authentication for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-administrate-data-authentication?view=azureml-api-2 |\n| Manage Azure RBAC roles for Azure ML workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-assign-roles?view=azureml-api-2 |\n| Authenticate and authorize access to AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-authenticate-batch-endpoint?view=azureml-api-2 |\n| Authenticate clients to Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-authenticate-online-endpoint?view=azureml-api-2 |\n| Configure authentication for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-authenticate-online-endpoint?view=azureml-api-2 |\n| Configure authentication for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-authenticate-online-endpoint?view=azureml-api-2 |\n| Use built-in Azure Policy to govern AI model deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-built-in-policy-model-deployment?view=azureml-api-2 |\n| Rotate Azure ML workspace storage account access keys | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-change-storage-access-key?view=azureml-api-2 |\n| Maintain network isolation with Azure ML v2 ARM APIs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-network-isolation-with-v2?view=azureml-api-2 |\n| Configure private endpoints for Azure ML workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-private-link?view=azureml-api-2 |\n| Create custom Azure Policies to restrict AI model deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-custom-policy-model-deployment?view=azureml-api-2 |\n| Use secret injection to access secrets in AML deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoint-with-secret-injection?view=azureml-api-2 |\n| Disable shared key access for Azure ML workspace storage | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-disable-local-auth-storage?view=azureml-api-2 |\n| Enable Azure ML studio access inside virtual networks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-enable-studio-virtual-network?view=azureml-api-2 |\n| Configure identity-based service authentication for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-identity-based-service-authentication?view=azureml-api-2 |\n| Configure identity-based service authentication for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-identity-based-service-authentication?view=azureml-api-2 |\n| Enforce Azure ML workspace compliance with Azure Policy | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-integrate-azure-policy?view=azureml-api-2 |\n| Configure Azure ML managed virtual network isolation | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-managed-network?view=azureml-api-2 |\n| Configure Model Catalog access with workspace managed virtual networks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-network-isolation-model-catalog?view=azureml-api-2 |\n| Secure Azure ML workspaces with virtual networks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-network-security-overview?view=azureml-api-2 |\n| Configure data exfiltration prevention for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-prevent-data-loss-exfiltration?view=azureml-api-2 |\n| Isolate Azure ML registries with VNets and private endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-registry-network-isolation?view=azureml-api-2 |\n| Configure network isolation for AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-batch-endpoint?view=azureml-api-2 |\n| Secure Azure ML online inferencing with VNets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-inferencing-vnet?view=azureml-api-2 |\n| Secure AKS inferencing environments for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-kubernetes-inferencing-environment?view=azureml-api-2 |\n| Configure TLS\u002FSSL for Azure ML Kubernetes endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-kubernetes-online-endpoint?view=azureml-api-2 |\n| Secure Azure ML managed online endpoints with network isolation | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-online-endpoint?view=azureml-api-2 |\n| Secure Azure ML RAG workflows with network isolation | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-rag-workflows?view=azureml-api-2 |\n| Secure Azure ML training environments with VNets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-training-vnet?view=azureml-api-2 |\n| Secure Azure ML workspace using virtual networks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-secure-workspace-vnet?view=azureml-api-2 |\n| Configure RBAC access to Azure ML feature store | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-setup-access-control-feature-store?view=azureml-api-2 |\n| Set up authentication to Azure ML workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-setup-authentication?view=azureml-api-2 |\n| Configure customer-managed keys for Azure ML resources | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-setup-customer-managed-keys?view=azureml-api-2 |\n| Securely use private Python packages in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-private-python-packages?view=azureml-api-1 |\n| Securely use Key Vault secrets in Azure ML runs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-secrets-in-runs?view=azureml-api-2 |\n| Apply built-in Azure Policy definitions for AML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fpolicy-reference?view=azureml-api-2 |\n| Manage API and data source credentials with prompt flow connections | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fconcept-connections?view=azureml-api-2 |\n| Secure prompt flow with virtual network isolation in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-secure-prompt-flow?view=azureml-api-2 |\n| Apply Azure Policy regulatory controls to Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fsecurity-controls-policy?view=azureml-api-2 |\n| Create secure Azure ML workspace in a VNet | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ftutorial-create-secure-workspace-vnet?view=azureml-api-2 |\n| Create a secure Azure ML workspace with managed VNet | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ftutorial-create-secure-workspace?view=azureml-api-2 |\n\n### Configuration\n| Topic | URL |\n|-------|-----|\n| Configure AutoML Classification component with ML Tables | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fclassification?view=azureml-api-2 |\n| Configure AutoML Forecasting component in designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fforecasting?view=azureml-api-2 |\n| Configure AutoML Image Multi-label Classification | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fimage-classification-multilabel?view=azureml-api-2 |\n| Configure AutoML Image Classification component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fimage-classification?view=azureml-api-2 |\n| Configure AutoML Image Instance Segmentation component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fimage-instance-segmentation?view=azureml-api-2 |\n| Configure AutoML Image Object Detection component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fimage-object-detection?view=azureml-api-2 |\n| Configure AutoML Regression component with ML Tables | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Fregression?view=azureml-api-2 |\n| Configure AutoML Text Multi-label Classification component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Ftext-classification-multilabel?view=azureml-api-2 |\n| Configure AutoML Text Classification component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Ftext-classification?view=azureml-api-2 |\n| Configure AutoML Text NER component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference-v2\u002Ftext-ner?view=azureml-api-2 |\n| Configure Add Columns component to concatenate datasets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fadd-columns?view=azureml-api-2 |\n| Configure Add Rows component to append dataset records | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fadd-rows?view=azureml-api-2 |\n| Configure Apply Image Transformation in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fapply-image-transformation?view=azureml-api-2 |\n| Configure Apply Math Operation component for column calculations | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fapply-math-operation?view=azureml-api-2 |\n| Configure Apply SQL Transformation component using SQLite | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fapply-sql-transformation?view=azureml-api-2 |\n| Configure Apply Transformation component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fapply-transformation?view=azureml-api-2 |\n| Configure Assign Data to Clusters in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fassign-data-to-clusters?view=azureml-api-2 |\n| Configure Boosted Decision Tree Regression component (LightGBM) | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fboosted-decision-tree-regression?view=azureml-api-2 |\n| Configure Clean Missing Data component for handling nulls | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fclean-missing-data?view=azureml-api-2 |\n| Configure Clip Values component to handle outliers | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fclip-values?view=azureml-api-2 |\n| Configure and use Azure ML designer algorithm components | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fcomponent-reference?view=azureml-api-2 |\n| Configure Convert to CSV component for dataset export | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fconvert-to-csv?view=azureml-api-2 |\n| Configure Convert to Dataset component for internal format | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fconvert-to-dataset?view=azureml-api-2 |\n| Configure Convert to Image Directory in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fconvert-to-image-directory?view=azureml-api-2 |\n| Configure Convert to Indicator Values for categorical encoding | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fconvert-to-indicator-values?view=azureml-api-2 |\n| Configure Convert Word to Vector component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fconvert-word-to-vector?view=azureml-api-2 |\n| Configure Create Python Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fcreate-python-model?view=azureml-api-2 |\n| Configure Cross Validate Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fcross-validate-model?view=azureml-api-2 |\n| Configure Decision Forest Regression in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fdecision-forest-regression?view=azureml-api-2 |\n| Configure DenseNet image classification component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fdensenet?view=azureml-api-2 |\n| Configure Edit Metadata component to adjust column roles | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fedit-metadata?view=azureml-api-2 |\n| Set up Enter Data Manually component for small datasets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fenter-data-manually?view=azureml-api-2 |\n| Configure Evaluate Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fevaluate-model?view=azureml-api-2 |\n| Configure Evaluate Recommender component for model accuracy | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fevaluate-recommender?view=azureml-api-2 |\n| Configure Execute Python Script in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fexecute-python-script?view=azureml-api-2 |\n| Configure Execute R Script component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fexecute-r-script?view=azureml-api-2 |\n| Configure Export Data component to save pipeline outputs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fexport-data?view=azureml-api-2 |\n| Configure Extract N-Gram Features from Text in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fextract-n-gram-features-from-text?view=azureml-api-2 |\n| Configure Fast Forest Quantile Regression in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ffast-forest-quantile-regression?view=azureml-api-2 |\n| Configure Feature Hashing text component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ffeature-hashing?view=azureml-api-2 |\n| Configure Filter Based Feature Selection for predictive columns | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ffilter-based-feature-selection?view=azureml-api-2 |\n| Use graph search query syntax in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fgraph-search-syntax?view=azureml-api-2 |\n| Configure Group Data into Bins component for discretization | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fgroup-data-into-bins?view=azureml-api-2 |\n| Configure Import Data component for Azure ML designer pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fimport-data?view=azureml-api-2 |\n| Configure Init Image Transformation in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Finit-image-transformation?view=azureml-api-2 |\n| Configure Join Data component to merge datasets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fjoin-data?view=azureml-api-2 |\n| Configure K-Means Clustering component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fk-means-clustering?view=azureml-api-2 |\n| Configure Latent Dirichlet Allocation component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Flatent-dirichlet-allocation?view=azureml-api-2 |\n| Configure Linear Regression component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Flinear-regression?view=azureml-api-2 |\n| Configure Multiclass Boosted Decision Tree in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fmulticlass-boosted-decision-tree?view=azureml-api-2 |\n| Configure Multiclass Decision Forest in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fmulticlass-decision-forest?view=azureml-api-2 |\n| Configure Multiclass Logistic Regression in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fmulticlass-logistic-regression?view=azureml-api-2 |\n| Configure Multiclass Neural Network in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fmulticlass-neural-network?view=azureml-api-2 |\n| Set up Neural Network Regression in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fneural-network-regression?view=azureml-api-2 |\n| Configure Normalize Data component for feature scaling | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fnormalize-data?view=azureml-api-2 |\n| Configure One-vs-All Multiclass component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fone-vs-all-multiclass?view=azureml-api-2 |\n| Configure One-vs-One Multiclass component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fone-vs-one-multiclass?view=azureml-api-2 |\n| Configure Partition and Sample component for dataset splitting | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fpartition-and-sample?view=azureml-api-2 |\n| Configure deprecated PCA-Based Anomaly Detection component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fpca-based-anomaly-detection?view=azureml-api-2 |\n| Configure Permutation Feature Importance component for model insights | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fpermutation-feature-importance?view=azureml-api-2 |\n| Use Poisson Regression component in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fpoisson-regression?view=azureml-api-2 |\n| Configure Preprocess Text component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fpreprocess-text?view=azureml-api-2 |\n| Configure Remove Duplicate Rows component for deduplication | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fremove-duplicate-rows?view=azureml-api-2 |\n| Configure ResNet image classification in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fresnet?view=azureml-api-2 |\n| Configure Score Image Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fscore-image-model?view=azureml-api-2 |\n| Configure Score Model component in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fscore-model?view=azureml-api-2 |\n| Configure Score SVD Recommender for predictions | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fscore-svd-recommender?view=azureml-api-2 |\n| Configure Score Vowpal Wabbit Model in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fscore-vowpal-wabbit-model?view=azureml-api-2 |\n| Configure Score Wide & Deep Recommender component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fscore-wide-and-deep-recommender?view=azureml-api-2 |\n| Configure Select Columns in Dataset to subset features | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fselect-columns-in-dataset?view=azureml-api-2 |\n| Configure Select Columns Transform for stable feature sets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fselect-columns-transform?view=azureml-api-2 |\n| Configure SMOTE component to oversample minority classes | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fsmote?view=azureml-api-2 |\n| Configure Split Data component for train-test partitioning | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fsplit-data?view=azureml-api-2 |\n| Configure Split Image Directory component for datasets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fsplit-image-directory?view=azureml-api-2 |\n| Configure Summarize Data component for descriptive statistics | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fsummarize-data?view=azureml-api-2 |\n| Configure Train Anomaly Detection Model component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-anomaly-detection-model?view=azureml-api-2 |\n| Configure Train Clustering Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-clustering-model?view=azureml-api-2 |\n| Configure Train PyTorch Model component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-pytorch-model?view=azureml-api-2 |\n| Configure Train SVD Recommender in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-svd-recommender?view=azureml-api-2 |\n| Configure Train Vowpal Wabbit Model in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-vowpal-wabbit-model?view=azureml-api-2 |\n| Configure Train Wide & Deep Recommender component | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftrain-wide-and-deep-recommender?view=azureml-api-2 |\n| Configure Tune Model Hyperparameters in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftune-model-hyperparameters?view=azureml-api-2 |\n| Configure Two-Class Averaged Perceptron in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-averaged-perceptron?view=azureml-api-2 |\n| Configure Two-Class Boosted Decision Tree in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-boosted-decision-tree?view=azureml-api-2 |\n| Configure Two-Class Decision Forest in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-decision-forest?view=azureml-api-2 |\n| Configure Two-Class Logistic Regression in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-logistic-regression?view=azureml-api-2 |\n| Configure Two-Class Neural Network in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-neural-network?view=azureml-api-2 |\n| Configure Two-Class SVM component in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Ftwo-class-support-vector-machine?view=azureml-api-2 |\n| Configure Web Service Input and Output components | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcomponent-reference\u002Fweb-service-input-output?view=azureml-api-2 |\n| Use expressions in Azure ML SDK and CLI v2 jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-expressions?view=azureml-api-2 |\n| Specify models for Azure ML online deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-online-deployment-model-specification?view=azureml-api-2 |\n| Use Azure ML prebuilt Docker images for inference | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-prebuilt-docker-images-inference?view=azureml-api-2 |\n| Configure and use Azure ML Responsible AI dashboard | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-responsible-ai-dashboard?view=azureml-api-2 |\n| Use workspace soft delete and recovery in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-soft-delete?view=azureml-api-2 |\n| Configure Git integration for Azure ML training jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-train-model-git-integration?view=azureml-api-2 |\n| Configure and use vector stores in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fconcept-vector-stores?view=azureml-api-2 |\n| Link OneLake tables to Azure ML via datastore UI | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fcreate-datastore-with-user-interface?view=azureml-api-2 |\n| Configure feature retrieval specs for training and inference | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ffeature-retrieval-concepts?view=azureml-api-2 |\n| Configure feature set materialization in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Ffeature-set-materialization-concepts?view=azureml-api-2 |\n| Access Azure cloud storage data during interactive ML development | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-access-data-interactive?view=azureml-api-2 |\n| Configure Kubernetes compute targets for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-attach-kubernetes-anywhere?view=azureml-api-2 |\n| Configure Kubernetes compute targets for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-attach-kubernetes-anywhere?view=azureml-api-2 |\n| Configure Kubernetes compute targets for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-attach-kubernetes-anywhere?view=azureml-api-2 |\n| Attach Kubernetes clusters to Azure ML workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-attach-kubernetes-to-workspace?view=azureml-api-2 |\n| Configure Azure AutoML for time-series forecasting | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-auto-train-forecast?view=azureml-api-2 |\n| Configure AutoML computer vision training in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-auto-train-image-models?view=azureml-api-2 |\n| Configure Azure AutoML for custom NLP training | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-auto-train-nlp-models?view=azureml-api-2 |\n| Configure autoscaling for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-autoscale-endpoints?view=azureml-api-2 |\n| Configure custom Azure Container for PyTorch environments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-azure-container-for-pytorch-environment?view=azureml-api-2 |\n| Enable production inference data collection for Azure ML endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-collect-production-data?view=azureml-api-2 |\n| Customize AutoML data featurization settings in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-auto-features?view=azureml-api-1 |\n| Configure Azure AutoML tabular training with SDK v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-auto-train?view=azureml-api-2 |\n| Configure data splits and cross-validation in Azure AutoML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-cross-validation-data-splits?view=azureml-api-1 |\n| Configure Azure ML connections to external data and services | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-connection?view=azureml-api-2 |\n| Create and manage Azure ML compute clusters | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-attach-compute-cluster?view=azureml-api-2 |\n| Configure and manage Azure ML compute in studio | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-attach-compute-studio?view=azureml-api-2 |\n| Create Azure ML compute instances for development | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-compute-instance?view=azureml-api-2 |\n| Create Azure ML compute instances for development | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-compute-instance?view=azureml-api-2 |\n| Create and manage Azure ML data assets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-data-assets?view=azureml-api-2 |\n| Create and manage Azure ML data assets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-data-assets?view=azureml-api-2 |\n| Configure image labeling projects in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-image-labeling-projects?view=azureml-api-2 |\n| Configure text labeling projects in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-text-labeling-projects?view=azureml-api-2 |\n| Create and configure vector indexes in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-vector-index?view=azureml-api-2 |\n| Create Azure ML workspaces with ARM templates | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-create-workspace-template?view=azureml-api-2 |\n| Configure custom DNS for Azure ML private endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-custom-dns?view=azureml-api-2 |\n| Customize Azure ML compute instances with startup scripts | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-customize-compute-instance?view=azureml-api-2 |\n| Configure and use Azure ML datastores for storage access | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-datastore?view=azureml-api-2 |\n| Deploy Azure ML extension on Kubernetes clusters | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-kubernetes-extension?view=azureml-api-2 |\n| Export or delete Azure ML workspace data | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-export-delete-data?view=azureml-api-2 |\n| Customize Azure ML prebuilt Docker images for inference | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-extend-prebuilt-docker-image-inference?view=azureml-api-1 |\n| Import external data into Azure ML as data assets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-import-data-assets?view=azureml-api-2 |\n| Label images and text in Azure ML projects | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-label-data?view=azureml-api-2 |\n| Link Synapse and Azure ML workspaces with Spark pools | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-link-synapse-ml-workspaces?view=azureml-api-1 |\n| Log MLflow models as first-class models in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-log-mlflow-models?view=azureml-api-2 |\n| Send Azure ML distributed training logs to Application Insights | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-log-search?view=azureml-api-2 |\n| Configure model interpretability in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-machine-learning-interpretability?view=azureml-api-2 |\n| Manage Azure ML compute instances and lifecycle | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-compute-instance?view=azureml-api-2 |\n| Configure Azure ML environments via CLI and SDK | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-environments-v2?view=azureml-api-2 |\n| Manage Azure ML environments via CLI and SDK | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-environments-v2?view=azureml-api-2 |\n| Create Azure ML hub workspaces with Bicep templates | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-hub-workspace-template?view=azureml-api-2 |\n| Manage lifecycle and auto-delete for imported data assets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-imported-data-assets?view=azureml-api-2 |\n| Manage component and pipeline inputs\u002Foutputs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-inputs-outputs-pipeline?view=azureml-api-2 |\n| Create and manage Azure ML Kubernetes instance types | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-kubernetes-instance-types?view=azureml-api-2 |\n| Administer and export Azure ML labeling projects | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-labeling-projects?view=azureml-api-2 |\n| Manage Azure ML model registry using MLflow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-models-mlflow?view=azureml-api-2 |\n| Register and manage models with Azure ML CLI and SDK | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-models?view=azureml-api-2 |\n| Create and manage Azure ML registries | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-registries?view=azureml-api-2 |\n| Manage Azure ML resources using the VS Code extension | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-resources-vscode?view=azureml-api-2 |\n| Attach and manage Synapse Spark pools in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-synapse-spark-pool?view=azureml-api-2 |\n| Provision Azure ML workspaces using Terraform | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-workspace-terraform?view=azureml-api-2 |\n| Configure data drift monitors in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-monitor-datasets?view=azureml-api-1 |\n| Collect and monitor Kubernetes endpoint inference logs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-monitor-kubernetes-online-enpoint-inference-server-log?view=azureml-api-2 |\n| Configure Azure ML model performance monitoring in production | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-monitor-model-performance?view=azureml-api-2 |\n| Configure monitoring and logging for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-monitor-online-endpoints?view=azureml-api-2 |\n| Configure monitoring and logging for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-monitor-online-endpoints?view=azureml-api-2 |\n| Extend Azure ML prebuilt inference images with Python | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-prebuilt-docker-images-inference-python-extensibility?view=azureml-api-1 |\n| Use R and RStudio on Azure Machine Learning compute | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-r-interactive-development?view=azureml-api-2 |\n| Use Responsible AI dashboard tools in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-responsible-ai-dashboard?view=azureml-api-2 |\n| Generate Responsible AI insights in Azure ML studio | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-responsible-ai-insights-ui?view=azureml-api-2 |\n| Configure and export Responsible AI scorecards in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-responsible-ai-scorecard?view=azureml-api-2 |\n| Schedule recurring data imports in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-schedule-data-import?view=azureml-api-2 |\n| Configure Azure ML training jobs and compute targets (v1) | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-set-up-training-targets?view=azureml-api-1 |\n| Share data assets across Azure ML workspaces via registries | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-share-data-across-workspaces-with-registries?view=azureml-api-2 |\n| Share models and components across Azure ML workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2 |\n| Query and compare MLflow experiments and runs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-track-experiments-mlflow?view=azureml-api-2 |\n| Submit MLflow Projects training jobs to Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-train-mlflow-projects?view=azureml-api-2 |\n| Configure and submit Azure ML training jobs (v2) | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-train-model?view=azureml-api-2 |\n| Configure and submit Azure ML training jobs (v2) | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-train-model?view=azureml-api-2 |\n| Train Azure ML models using custom Docker images (v1) | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-train-with-custom-image?view=azureml-api-1 |\n| Configure hyperparameter sweep jobs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-tune-hyperparameters?view=azureml-api-2 |\n| Configure AutoMLStep in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-automlstep-in-pipelines?view=azureml-api-1 |\n| Use MLflow to track Azure ML experiments and runs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-mlflow-cli-runs?view=azureml-api-2 |\n| Configure MLflow tracking with Azure Machine Learning workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-mlflow-configure-tracking?view=azureml-api-2 |\n| Configure and run parallel jobs in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-parallel-job-in-pipeline?view=azureml-api-2 |\n| Configure pipeline parameters in Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-pipeline-parameter?view=azureml-api-1 |\n| Run training jobs on Azure ML serverless compute | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-serverless-compute?view=azureml-api-2 |\n| Configure hyperparameter sweep in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-sweep-in-pipeline?view=azureml-api-2 |\n| Configure dataset versioning in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-version-track-datasets?view=azureml-api-1 |\n| View and tag costs for Azure ML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-view-online-endpoints-costs?view=azureml-api-2 |\n| Configure serverless Spark compute for Azure ML notebooks | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Finteractive-data-wrangling-with-apache-spark-azure-ml?view=azureml-api-2 |\n| Reference Azure Machine Learning monitoring metrics and logs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmonitor-azure-machine-learning-reference?view=azureml-api-2 |\n| Configure custom base images for prompt flow sessions | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-customize-session-base-image?view=azureml-api-2 |\n| Configure and consume streaming responses from prompt flow endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-enable-streaming-mode?view=azureml-api-2 |\n| Enable tracing and user feedback collection for prompt flow deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-enable-trace-feedback-for-deployment?view=azureml-api-2 |\n| Configure and manage prompt flow compute sessions in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-manage-compute-session?view=azureml-api-2 |\n| Configure monitoring for Azure ML generative AI apps | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-monitor-generative-ai-applications?view=azureml-api-2 |\n| Configure Azure OpenAI GPT-4 Turbo with Vision tool | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fazure-open-ai-gpt-4v-tool?view=azureml-api-2 |\n| Configure Content Safety text tool in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fcontent-safety-text-tool?view=azureml-api-2 |\n| Configure embedding tool for OpenAI in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fembedding-tool?view=azureml-api-2 |\n| Configure Index Lookup tool for RAG in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Findex-lookup-tool?view=azureml-api-2 |\n| Configure LLM tool in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fllm-tool?view=azureml-api-2 |\n| Use Open Model LLM tool in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fopen-model-llm-tool?view=azureml-api-2 |\n| Configure OpenAI GPT-4V tool in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fopenai-gpt-4v-tool?view=azureml-api-2 |\n| Configure and manage tools in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Foverview?view=azureml-api-2 |\n| Use and configure prompt templates in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fprompt-tool?view=azureml-api-2 |\n| Create and configure Python tools in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fpython-tool?view=azureml-api-2 |\n| Configure Rerank tool for RAG in prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Frerank-tool?view=azureml-api-2 |\n| Configure SerpAPI search tool in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Ftools-reference\u002Fserp-api-tool?view=azureml-api-2 |\n| Configure Automated ML forecasting jobs via YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automated-ml-forecasting?view=azureml-api-2 |\n| Author AutoML image classification jobs in YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-cli-classification?view=azureml-api-2 |\n| Define AutoML image instance segmentation YAML jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-cli-instance-segmentation?view=azureml-api-2 |\n| Configure AutoML image multilabel classification YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-cli-multilabel-classification?view=azureml-api-2 |\n| Author AutoML image object detection YAML jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-cli-object-detection?view=azureml-api-2 |\n| Configure AutoML vision hyperparameters in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-hyperparameters?view=azureml-api-2 |\n| Format JSONL data for AutoML computer vision | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-images-schema?view=azureml-api-2 |\n| Configure AutoML multilabel text classification YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-nlp-cli-multilabel-classification?view=azureml-api-2 |\n| Author AutoML NLP NER jobs using YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-nlp-cli-ner?view=azureml-api-2 |\n| Define AutoML text classification jobs with YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-automl-nlp-cli-text-classification?view=azureml-api-2 |\n| Reference configuration for Kubernetes with Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-kubernetes?view=azureml-api-2 |\n| Define command components via Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-component-command?view=azureml-api-2 |\n| Author pipeline components using Azure ML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-component-pipeline?view=azureml-api-2 |\n| Configure Spark components in Azure ML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-component-spark?view=azureml-api-2 |\n| Configure AmlCompute clusters via YAML in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-compute-aml?view=azureml-api-2 |\n| Define Azure ML compute instances with YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-compute-instance?view=azureml-api-2 |\n| Configure attached Kubernetes clusters in Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-compute-kubernetes?view=azureml-api-2 |\n| Attach and configure VMs via Azure ML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-compute-vm?view=azureml-api-2 |\n| Configure AI Content Safety connections in AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-ai-content-safety?view=azureml-api-2 |\n| Author AI Search connection YAML for AML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-ai-search?view=azureml-api-2 |\n| Configure Foundry Tools connections with Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-ai-services?view=azureml-api-2 |\n| Define API key connections via AML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-api-key?view=azureml-api-2 |\n| Define Azure OpenAI connections via AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-azure-openai?view=azureml-api-2 |\n| Define blob datastore connections in AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-blob?view=azureml-api-2 |\n| Configure Azure Container Registry connections in AML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-container-registry?view=azureml-api-2 |\n| Author custom key connections in Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-custom-key?view=azureml-api-2 |\n| Configure Data Lake Gen2 connections via AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-data-lake?view=azureml-api-2 |\n| Configure Git repository connections in AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-git?view=azureml-api-2 |\n| Set up OneLake connections using AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-onelake?view=azureml-api-2 |\n| Configure OpenAI service connections in AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-openai?view=azureml-api-2 |\n| Set up Python feed connections using AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-python-feed?view=azureml-api-2 |\n| Define Serp connections via Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-serp?view=azureml-api-2 |\n| Author serverless connection YAML for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-serverless?view=azureml-api-2 |\n| Configure AI Speech Services connections in AML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-connection-speech?view=azureml-api-2 |\n| Understand core Azure ML CLI v2 YAML syntax | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-core-syntax?view=azureml-api-2 |\n| Reference schema for Azure ML data YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-data?view=azureml-api-2 |\n| Define Azure Blob datastores via YAML in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-datastore-blob?view=azureml-api-2 |\n| Author Azure Data Lake Gen1 datastore YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-datastore-data-lake-gen1?view=azureml-api-2 |\n| Configure Azure Data Lake Gen2 datastores in YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-datastore-data-lake-gen2?view=azureml-api-2 |\n| Configure Azure Files datastores using YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-datastore-files?view=azureml-api-2 |\n| Author batch deployment YAML for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-deployment-batch?view=azureml-api-2 |\n| Define Kubernetes online deployments in Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-deployment-kubernetes-online?view=azureml-api-2 |\n| Configure managed online deployments via YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-deployment-managed-online?view=azureml-api-2 |\n| Author deployment template YAML for Azure ML CLI v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-deployment-template?view=azureml-api-2 |\n| Author batch endpoint YAML for Azure ML CLI v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-endpoint-batch?view=azureml-api-2 |\n| Configure Azure ML online endpoints with YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-endpoint-online?view=azureml-api-2 |\n| Reference schema for Azure ML environment YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-environment?view=azureml-api-2 |\n| Author feature entity definitions via Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-feature-entity?view=azureml-api-2 |\n| Create feature retrieval specs with Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-feature-retrieval-spec?view=azureml-api-2 |\n| Configure feature sets in Azure ML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-feature-set?view=azureml-api-2 |\n| Define feature stores in Azure ML using YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-feature-store?view=azureml-api-2 |\n| Define feature set specifications using YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-featureset-spec?view=azureml-api-2 |\n| Author command job YAML for Azure ML CLI v2 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-command?view=azureml-api-2 |\n| Create parallel jobs in Azure ML pipeline YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-parallel?view=azureml-api-2 |\n| Author pipeline job definitions with AML YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-pipeline?view=azureml-api-2 |\n| Configure Azure ML pipeline jobs using YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-pipeline?view=azureml-api-2 |\n| Configure Spark jobs in Azure ML with YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-spark?view=azureml-api-2 |\n| Define sweep (hyperparameter) jobs with Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-job-sweep?view=azureml-api-2 |\n| Reference schema for Azure ML MLTable YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-mltable?view=azureml-api-2 |\n| Define Azure ML models using CLI v2 YAML schema | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-model?view=azureml-api-2 |\n| Create model monitoring schedules with Azure ML YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-monitor?view=azureml-api-2 |\n| Navigate Azure ML CLI v2 YAML schema references | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-overview?view=azureml-api-2 |\n| Define Azure ML registries using CLI v2 YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-registry?view=azureml-api-2 |\n| Author data import schedule YAML for Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-schedule-data-import?view=azureml-api-2 |\n| Configure Azure ML job schedules with YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-schedule?view=azureml-api-2 |\n| Reference schema for Azure ML workspace YAML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-yaml-workspace?view=azureml-api-2 |\n\n### Integrations & Coding Patterns\n| Topic | URL |\n|-------|-----|\n| Configure input data sources for AML batch endpoint jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-access-data-batch-endpoints-jobs?view=azureml-api-2 |\n| Set up Azure Databricks with AutoML in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-configure-databricks-automl-environment?view=azureml-api-1 |\n| Connect storage to Azure ML via studio UI | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-connect-data-ui?view=azureml-api-1 |\n| Ingest data to Azure ML with Data Factory | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-data-ingest-adf?view=azureml-api-1 |\n| Wrangle data using Synapse Spark with Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-data-prep-synapse-spark-pool?view=azureml-api-1 |\n| Configure Azure ML datastores for storage access | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-datastore?view=azureml-api-2 |\n| Configure Azure ML datastores for storage access | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-datastore?view=azureml-api-2 |\n| Deploy AML models as custom skills for Azure AI Search | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-model-cognitive-search?view=azureml-api-1 |\n| Deploy Hugging Face transformer models to Azure ML endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-models-from-huggingface?view=azureml-api-2 |\n| Use Azure ML REST API for online deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-with-rest?view=azureml-api-2 |\n| Import data into Azure ML designer | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-designer-import-data?view=azureml-api-1 |\n| Run custom Python code in Azure ML designer pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-designer-python?view=azureml-api-1 |\n| Run local ONNX inference for Azure AutoML image models | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-inference-onnx-automl-image-models?view=azureml-api-2 |\n| Use Azure ML inference HTTP server for local debugging | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-inference-server-http?view=azureml-api-2 |\n| Log metrics and artifacts with MLflow in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-log-view-metrics?view=azureml-api-2 |\n| Manage Azure ML resources using REST APIs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-manage-rest?view=azureml-api-2 |\n| Define and use MLTable data in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-mltable?view=azureml-api-2 |\n| Securely integrate Azure Synapse with Azure ML via VNets | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-private-endpoint-integration-synapse?view=azureml-api-2 |\n| Read and write data in Azure ML jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-read-write-data-v2?view=azureml-api-2 |\n| Read and write data in Azure ML jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-read-write-data-v2?view=azureml-api-2 |\n| Generate Responsible AI dashboards with Azure ML SDK | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-responsible-ai-insights-sdk-cli?view=azureml-api-2 |\n| Attach secured Azure Databricks to Azure ML via private endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-securely-attach-databricks?view=azureml-api-2 |\n| Submit standalone and pipeline Spark jobs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-submit-spark-jobs?view=azureml-api-2 |\n| Log metrics in Azure ML designer pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-track-designer-experiments?view=azureml-api-1 |\n| Use Azure AutoML ONNX models with ML.NET in .NET apps | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-automl-onnx-model-dotnet?view=azureml-api-2 |\n| Invoke Azure ML batch endpoints from Azure Data Factory | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-azure-data-factory?view=azureml-api-2 |\n| Access Azure ML batch endpoints from Microsoft Fabric | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-fabric?view=azureml-api-2 |\n| Trigger AML batch endpoints from Event Grid and storage events | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-event-grid-batch?view=azureml-api-2 |\n| Integrate Azure ML events with Azure Event Grid | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-event-grid?view=azureml-api-2 |\n| Use labeled datasets from Azure ML labeling | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-labeled-dataset?view=azureml-api-1 |\n| Integrate Azure Databricks MLflow tracking with Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-mlflow-azure-databricks?view=azureml-api-2 |\n| Configure MLflow tracking from Azure Synapse to Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-mlflow-azure-synapse?view=azureml-api-2 |\n| Integrate Azure Synapse Spark in Azure ML pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-synapsesparkstep?view=azureml-api-1 |\n| Create and use custom tool packages in Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-custom-tool-package-creation-and-usage?view=azureml-api-2 |\n| Evaluate Semantic Kernel plugins and planners with prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-evaluate-semantic-kernel?view=azureml-api-2 |\n| Integrate LangChain workflows with Azure ML prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-integrate-with-langchain?view=azureml-api-2 |\n| Incorporate image inputs into Azure ML prompt flows | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fprompt-flow\u002Fhow-to-process-image?view=azureml-api-2 |\n| Quickstart: Configure Spark jobs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fquickstart-spark-jobs?view=azureml-api-2 |\n| Map Azure ML v1 logging APIs to MLflow tracking | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Freference-migrate-sdk-v1-mlflow-tracking?view=azureml-api-2 |\n\n### Deployment\n| Topic | URL |\n|-------|-----|\n| Consume Azure ML standard deployments across workspaces | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-connect-models-serverless?view=azureml-api-2 |\n| Convert ML notebooks to production scripts with MLOpsPython | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-convert-ml-experiment-to-production?view=azureml-api-1 |\n| Deploy AutoML models to AML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-automl-endpoint?view=azureml-api-2 |\n| Deploy AML models to Azure Container Instances with CLI v1 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-azure-container-instance?view=azureml-api-1 |\n| Deploy AML models to Azure Kubernetes Service with SDK\u002FCLI v1 | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-azure-kubernetes-service?view=azureml-api-1 |\n| Deploy custom-container models to AML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-custom-container?view=azureml-api-2 |\n| Run MLflow models in Azure ML Spark jobs | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-mlflow-model-spark-jobs?view=azureml-api-2 |\n| Progressively roll out MLflow models on Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-mlflow-models-online-progressive?view=azureml-api-2 |\n| Deploy MLflow models to Azure ML endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-mlflow-models?view=azureml-api-2 |\n| Customize batch deployment outputs in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-model-custom-output?view=azureml-api-2 |\n| Deploy catalog models as standard deployments in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-models-serverless?view=azureml-api-2 |\n| Deploy machine learning models to Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy models to AML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy models to AML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy models to AML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy models to AML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy models to AML managed online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-online-endpoints?view=azureml-api-2 |\n| Deploy Azure ML pipelines as batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-pipeline-component-as-batch-endpoint?view=azureml-api-2 |\n| Publish and run Azure ML pipelines in production | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-pipelines?view=azureml-api-1 |\n| Serve models with NVIDIA Triton on AML endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-with-triton?view=azureml-api-2 |\n| Build Azure ML CI\u002FCD pipelines with Azure DevOps | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-devops-machine-learning?view=azureml-api-2 |\n| Create GitHub Actions workflows for Azure ML CI\u002FCD | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-github-actions-machine-learning?view=azureml-api-2 |\n| Deploy image-processing models with AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-image-processing-batch?view=azureml-api-2 |\n| Deploy MLflow models for batch inference with Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-mlflow-batch?view=azureml-api-2 |\n| Run language models with AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-nlp-processing-batch?view=azureml-api-2 |\n| Retrain Azure ML designer models via published pipelines | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-retrain-designer?view=azureml-api-1 |\n| Run Azure ML RAG prompt flows locally with VS Code | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-retrieval-augmented-generation-cloud-to-local?view=azureml-api-2 |\n| Deploy and trigger batch prediction pipelines in Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-run-batch-predictions-designer?view=azureml-api-1 |\n| Perform safe blue-green rollouts for Azure ML online endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-safely-rollout-online-endpoints?view=azureml-api-2 |\n| Set up end-to-end MLOps with Azure DevOps and Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-setup-mlops-azureml?view=azureml-api-2 |\n| Set up end-to-end MLOps with GitHub and Azure ML | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-setup-mlops-github-azure-ml?view=azureml-api-2 |\n| Trigger published Azure ML pipelines automatically | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-trigger-published-pipeline?view=azureml-api-1 |\n| Deploy models for batch scoring with AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-model-deployments?view=azureml-api-2 |\n| Run Azure OpenAI embeddings via AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-model-openai-embeddings?view=azureml-api-2 |\n| Deploy and invoke pipelines via AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-pipeline-deployments?view=azureml-api-2 |\n| Convert existing AML pipeline jobs to batch endpoint deployments | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-pipeline-from-job?view=azureml-api-2 |\n| Operationalize scoring pipelines on AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-scoring-pipeline?view=azureml-api-2 |\n| Operationalize training pipelines on AML batch endpoints | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-batch-training-pipeline?view=azureml-api-2 |\n| Build RAG pipelines with Azure ML and prompt flow | https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-use-pipelines-prompt-flow?view=azureml-api-2 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