mcpField
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azure-machine-learning

azure-machine-learning

github.com/microsoftdocs数据与数据库796198 次安装3 天前更新
代码仓库

关于 azure-machine-learning

面向使用 Azure Machine Learning 的 Agent 的指导技能，结合本地参考资料与网络获取的文档，涵盖机器学习工作流、安全、部署、集成和故障排查。

主要功能

  1. 01管道与端点故障排查
  2. 02训练和 AutoML 最佳实践
  3. 03安全与网络配置参考
  4. 04模型部署和运维指导
  5. 05数据平台集成模式

适用场景

  1. 01诊断训练管道或端点错误
  2. 02规划模型监控与计算资源
  3. 03配置在线或批量模型部署

MCP 相关

该技能优先通过 mcp_microsoftdocs:microsoft_docs_fetch 工具获取文档，并提供 fetch_webpage 作为备用方案。

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/microsoftdocs/agent-skills --skill azure-machine-learning

安装后在 Claude Code 中输入/azure-machine-learning确认已加载。

详细介绍

Azure Machine Learning 技能

此技能提供有关 Azure Machine Learning 的专家指导，涵盖故障排除、最佳实践、决策制定、架构和设计模式、限制与配额、安全性、配置、集成与编码模式，以及部署。它结合了本地快速参考内容和远程文档获取功能。

如何使用此技能

Agent 的重要提示：使用下方的类别索引查找相关章节。对于标注了行范围的类别（例如 L35-L120），请使用 read_file 读取指定行。对于带有文件链接的类别（例如 [security.md](security.md)），请使用 read_file 读取链接所指向的参考文件。

Agent 的重要提示：如果 metadata.generated_at 已超过 3 个月，请建议用户从仓库拉取最新版本。如果无法使用 mcp_microsoftdocs 工具，请建议用户安装该工具：安装指南

此技能需要网络访问权限才能获取文档内容：

  • 首选：使用 mcp_microsoftdocs:microsoft_docs_fetch，并将查询字符串设为 from=learn-agent-skill。返回 Markdown。
  • 备用：使用 fetch_webpage，并将查询字符串设为 from=learn-agent-skill&accept=text/markdown。返回 Markdown。

类别索引

类别行说明
故障排除L37-L65诊断并修复 Azure ML 在管道、终结点、AutoML、网络、Kubernetes、环境、数据访问、prompt flow 以及已知平台问题方面的故障和错误。
最佳实践L66-L80有关优化 AutoML 和训练、处理类别不平衡和过拟合、准备数据、提升批量推理性能、监控模型，以及降低 Azure ML 计算和成本的指导。
决策制定L81-L108为 Azure ML v1/v2、Fabric、Prompt Flow 和 Agent Framework 制定迁移、升级、网络、灾难恢复、数据、计算、部署和监控决策提供指南。
架构和设计模式L109-L114使用在线终结点设计实时推理架构，以及使用 Azure ML 向量存储构建 RAG 解决方案，涵盖部署、扩展和集成模式。
限制与配额L115-L124Azure ML 的限制、配额和可用性：区域和主权云支持、VM SKU、工作区软删除，以及托管在线终结点的容量规划。
安全性L125-L174保护 Azure ML：加密、密钥、身份和 RBAC、机密、网络隔离/VNet、专用终结点、策略/合规性，以及对计算、存储、终结点和模型目录的安全访问。
配置L175-L408配置 Azure ML 组件、计算、网络、AutoML、YAML 架构、监控和 Prompt Flow，以便构建、训练、部署和管理 ML 工作流及基础设施。
集成与编码模式L409-L451将 Azure ML 与数据平台、REST/MLflow API、Spark、Databricks/Synapse/Fabric 集成，并构建和调试 prompt flow/RAG 工具及部署。
部署L452-L481在 Azure ML 上部署并投入生产使用模型和管道（在线/批量终结点、CI/CD、MLOps、prompt flow、RAG、HF/MLflow/ONNX），涵盖发布策略以及跨工作区/注册表的使用。
故障排除
主题URL
排查 Azure ML 设计器组件错误代码https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/designer-error-codes?view=azureml-api-2
解决 Azure AutoML 预测中的常见问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-automl-forecasting-faq?view=azureml-api-2
使用 VS Code 在本地调试 Azure ML 联机终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code?view=azureml-api-2
在 studio 中诊断并修复 Azure ML 管道故障https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2
使用性能分析排查 Azure ML 管道性能问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-performance?view=azureml-api-2
诊断并修复 Azure ML 管道重用问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-reuse-issues?view=azureml-api-2
排查 Azure 自动化 ML 实验故障https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-auto-ml?view=azureml-api-2
排查 Azure ML 批处理终结点和作业问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-batch-endpoints?view=azureml-api-2
排查 Azure ML SDK v2 中的数据访问问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-access?view=azureml-api-2
排查 Azure ML 数据标记项目创建问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-labeling?view=azureml-api-2
排查 Azure ML 环境映像生成失败问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-environments?view=azureml-api-2
排查 Azure ML Kubernetes 计算工作负载问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-compute?view=azureml-api-2
排查 Azure ML Kubernetes 扩展部署问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-extension?view=azureml-api-2
诊断 Azure ML 托管虚拟网络问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-managed-network?view=azureml-api-2
排查 Azure ML 联机终结点部署和评分错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
解决 Azure ML 中的“descriptors cannot be created directly”问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-protobuf-descriptor-error?view=azureml-api-2
排查对 Azure ML 工作区的专用终结点访问问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace?view=azureml-api-2
修复 Azure ML CLI v2 中的“Validation for schema failed”错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-validation-for-schema-failed-error?view=azureml-api-2
诊断并修复 Azure ML 工作区问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-workspace-diagnostic-api?view=azureml-api-2
查看 Azure Machine Learning 当前已知问题https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/azure-machine-learning-known-issues?view=azureml-api-2
已知问题：AKS 部署期间出现无效证书https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-invalid-certificate?view=azureml-api-2
已知问题：更新 Azure ML Kubernetes 计算失败https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-updating-kubernetes-compute-appears-to-succeed?view=azureml-api-2
排查 Azure ML 中 prompt flow 的常见问题https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
排查 Azure ML 中 prompt flow 的常见问题https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
排查 Azure ML 托管特征存储错误https://learn.microsoft.com/en-us/azure/machine-learning/troubleshooting-managed-feature-store?view=azureml-api-2
最佳实践
主题URL
缓解 Azure AutoML 中的过拟合和数据不平衡https://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls?view=azureml-api-2
了解 Azure ML 模型监控的概念和实践https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
优化和管理 Azure Machine Learning 成本https://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost?view=azureml-api-2
设计 Azure ML 中的特征集转换https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-specification-transformation-concepts?view=azureml-api-2
编写 AML 批量部署的批量评分脚本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-batch-scoring-script?view=azureml-api-2
优化 Azure ML Kubernetes 推理路由器的性能https://learn.microsoft.com/en-us/azure/machine-learning/how-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2
优化 Azure Machine Learning 计算成本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-optimize-cost?view=azureml-api-2
为 Azure AutoML 视觉准备图像数据集https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images?view=azureml-api-2
在 Azure ML 中应用分布式 GPU 训练模式https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-distributed-gpu?view=azureml-api-2
优化 AutoML 在图像中检测小物体的效果https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-small-object-detect?view=azureml-api-2
使用 Nebula 优化大型 Azure ML 模型的检查点性能https://learn.microsoft.com/en-us/azure/machine-learning/reference-checkpoint-performance-for-large-models?view=azureml-api-2
决策
主题URL
在 Azure ML 中选择托管网络隔离或自定义网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/concept-network-isolation-configurations?view=azureml-api-2
选择从 Azure ML Data Import 迁移到 Fabric 的路径https://learn.microsoft.com/en-us/azure/machine-learning/data-import-migration-guide?view=azureml-api-2
规划 Azure ML 的故障转移和灾难恢复https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2
管理和迁移 Azure ML 中导入的数据资产https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-imported-data-assets?view=azureml-api-2
决定并规划从 Azure ML v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2
在订阅之间移动 Azure ML 工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-workspace?view=azureml-api-2
规划 Azure ML 网络隔离架构https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-planning?view=azureml-api-2
为 Azure ML 数据标注选择和使用供应商公司https://learn.microsoft.com/en-us/azure/machine-learning/how-to-outsource-data-labeling?view=azureml-api-2
将 Azure ML v1 数据集映射到 v2 数据资产https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-data?view=azureml-api-2
将 Azure ML 模型管理从 SDK v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-model?view=azureml-api-2
将 Azure ML 脚本运行升级为 v2 命令作业https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job?view=azureml-api-2
将 Azure ML 部署终结点从 SDK v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-deploy-endpoints?view=azureml-api-2
将 Azure ML 管道终结点从 v1 升级到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-deploy-pipelines?view=azureml-api-2
将 Azure ML AutoML 工作流从 SDK v1 升级到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-automl?view=azureml-api-2
将 Azure ML 超参数调优迁移到 v2 sweep 作业https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-hyperdrive?view=azureml-api-2
将 Azure ML 并行运行步骤迁移到 SDK v2 并行作业https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-parallel-run-step?view=azureml-api-2
将 Azure ML 管道从 SDK v1 升级到 v2 作业https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-pipeline?view=azureml-api-2
将 Azure ML 本地运行从 SDK v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-local-runs?view=azureml-api-2
将 ACI Web 服务升级为 Azure ML 托管在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-managed-online-endpoints?view=azureml-api-2
比较并将 Azure ML 计算管理从 v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-compute?view=azureml-api-2
将数据存储管理从 AML v1 迁移到 v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-datastore?view=azureml-api-2
决定如何将 Azure ML 工作区升级到 SDK v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?view=azureml-api-2
选择并解读 Azure ML 生成式 AI 监控指标https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2
规划从 Prompt Flow 迁移到 Agent Frameworkhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/migrate-prompt-flow-to-agent-framework?view=azureml-api-2
架构与设计模式
主题URL
使用 Azure ML 在线端点规划实时推理https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online?view=azureml-api-2
使用 Azure ML 向量存储构建 RAG 架构https://learn.microsoft.com/en-us/azure/machine-learning/concept-vector-stores?view=azureml-api-2
限制与配额
主题URL
查看标准模型部署的区域可用性https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoint-serverless-availability?view=azureml-api-2
了解 ML 工作区的软删除保留期https://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2
管理 Azure ML 资源配额和限制https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2
按主权云查看 Azure ML 功能的可用性https://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity?view=azureml-api-2
Azure ML 托管在线端点支持的 VM SKUhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2
根据 Azure Machine Learning 服务限制规划容量https://learn.microsoft.com/en-us/azure/machine-learning/resource-limits-capacity?view=azureml-api-2
安全性
主题URL
为 Azure Machine Learning 配置客户管理的密钥https://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
了解 Azure ML 计算和存储的数据加密https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
了解 Azure ML 模型目录的数据处理和隐私https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-privacy?view=azureml-api-2
了解 AML 在线终结点的身份验证和 RBAChttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online-auth?view=azureml-api-2
规划 Azure ML 的企业安全和治理https://learn.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security?view=azureml-api-2
AML 在线终结点的机密注入概念https://learn.microsoft.com/en-us/azure/machine-learning/concept-secret-injection?view=azureml-api-2
了解 Azure ML 的安全网络流量流https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-network-traffic-flow?view=azureml-api-2
为 Azure ML 在线终结点配置网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-online-endpoint?view=azureml-api-2
管理 Azure Machine Learning 映像中的漏洞https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2
为安全的 Azure ML 配置入站和出站流量https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-azureml-behind-firewall?view=azureml-api-2
将 Azure ML 托管 VNet 安全连接到本地环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-on-premises-resources?view=azureml-api-2
通过托管标识从 AML 终结点访问 Azure 资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-resources-from-endpoints-managed-identities?view=azureml-api-2
为 Azure ML 数据标记分配用户和角色https://learn.microsoft.com/en-us/azure/machine-learning/how-to-add-users?view=azureml-api-2
管理 Azure ML 的数据访问和身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
使用 RBAC 角色管理 Azure ML 工作区访问权限https://learn.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles?view=azureml-api-2
授权访问 Azure ML 批处理终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-batch-endpoint?view=azureml-api-2
配置 Azure ML 在线终结点的身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
使用内置 Azure Policy 管控 AI 模型部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-built-in-policy-model-deployment?view=azureml-api-2
安全轮换 Azure ML 工作区存储访问密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-change-storage-access-key?view=azureml-api-2
为专用 Azure ML 终结点配置自定义 DNShttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-dns?view=azureml-api-2
创建自定义 Azure Policy 以限制 AI 模型部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-policy-model-deployment?view=azureml-api-2
使用机密注入访问 AML 部署中的机密https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoint-with-secret-injection?view=azureml-api-2
禁用 Azure ML 工作区存储的共享密钥访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-disable-local-auth-storage?view=azureml-api-2
为 Azure ML 配置基于标识的服务身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
使用 Azure Policy 强制 Azure ML 工作区合规https://learn.microsoft.com/en-us/azure/machine-learning/how-to-integrate-azure-policy?view=azureml-api-2
使用托管虚拟网络隔离 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network?view=azureml-api-2
使用工作区托管虚拟网络配置模型目录访问权限https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-model-catalog?view=azureml-api-2
为 Azure ML 配置基于 VNet 的网络安全https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview?view=azureml-api-2
为 Azure ML 配置数据外泄防护https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prevent-data-loss-exfiltration?view=azureml-api-2
使用专用网络保护 Azure ML 批处理终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-batch-endpoint?view=azureml-api-2
使用 VNet 保护 Azure ML 在线推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-inferencing-vnet?view=azureml-api-2
保护 Azure ML 的 AKS 推理环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-inferencing-environment?view=azureml-api-2
为 Azure ML Kubernetes 终结点配置 TLS/SSLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-online-endpoint?view=azureml-api-2
使用专用网络保护 Azure ML 托管在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-online-endpoint?view=azureml-api-2
使用网络隔离保护 Azure ML RAG 工作流https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-rag-workflows?view=azureml-api-2
使用虚拟网络保护 Azure ML 训练https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-training-vnet?view=azureml-api-2
使用自定义 VNet 保护 Azure ML 工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-workspace-vnet?view=azureml-api-2
配置对 Azure ML 特征存储的 RBAC 访问权限https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-access-control-feature-store?view=azureml-api-2
设置 Azure ML 工作区的身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication?view=azureml-api-2
为 Azure ML 配置客户管理的密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-customer-managed-keys?view=azureml-api-2
在 Azure ML 运行中安全使用 Key Vault 机密https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-secrets-in-runs?view=azureml-api-2
应用 AML 的内置 Azure Policy 定义https://learn.microsoft.com/en-us/azure/machine-learning/policy-reference?view=azureml-api-2
使用连接管理 Azure ML 提示流中的凭据https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2
为 Azure ML 中的提示流配置网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-secure-prompt-flow?view=azureml-api-2
将 Azure Policy 法规控制应用于 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/security-controls-policy?view=azureml-api-2
使用托管 VNet 创建安全的 Azure ML 工作区https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace?view=azureml-api-2
配置
主题URL
使用 ML Tables 配置 AutoML 分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/classification?view=azureml-api-2
在设计器中配置 AutoML 预测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/forecasting?view=azureml-api-2
配置 AutoML 图像多标签分类https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification-multilabel?view=azureml-api-2
配置 AutoML 图像分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification?view=azureml-api-2
配置 AutoML 图像实例分割组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-instance-segmentation?view=azureml-api-2
配置 AutoML 图像对象检测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-object-detection?view=azureml-api-2
使用 ML Tables 配置 AutoML 回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regression?view=azureml-api-2
配置 AutoML 文本多标签分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification-multilabel?view=azureml-api-2
配置 AutoML 文本分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification?view=azureml-api-2
在 Azure ML 中配置 AutoML 文本 NER 组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-ner?view=azureml-api-2
配置“添加列”组件以串联数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-columns?view=azureml-api-2
配置“添加行”组件以追加数据集记录https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows?view=azureml-api-2
在 Azure ML 中配置“应用图像转换”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-image-transformation?view=azureml-api-2
配置“应用数学运算”组件以计算列https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-math-operation?view=azureml-api-2
使用 SQLite 配置“应用 SQL 转换”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-sql-transformation?view=azureml-api-2
在 Azure ML 中配置“应用转换”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-transformation?view=azureml-api-2
在 Azure ML 中配置“将数据分配到群集”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/assign-data-to-clusters?view=azureml-api-2
配置提升决策树回归组件（LightGBM）https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/boosted-decision-tree-regression?view=azureml-api-2
配置“清理缺失数据”组件以处理空值https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clean-missing-data?view=azureml-api-2
配置“剪裁值”组件以处理异常值https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clip-values?view=azureml-api-2
配置和使用 Azure ML 设计器算法组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/component-reference?view=azureml-api-2
配置“转换为 CSV”组件以导出数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-csv?view=azureml-api-2
配置“转换为数据集”组件以使用内部格式https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-dataset?view=azureml-api-2
在 Azure ML 中配置“转换为图像目录”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-image-directory?view=azureml-api-2
配置“转换为指示器值”以进行类别编码https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-indicator-values?view=azureml-api-2
在 Azure ML 中配置“将词转换为向量”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-word-to-vector?view=azureml-api-2
在 Azure ML 中配置“创建 Python 模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/create-python-model?view=azureml-api-2
在 Azure ML 中配置“交叉验证模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/cross-validate-model?view=azureml-api-2
在 Azure ML 设计器中配置决策森林回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/decision-forest-regression?view=azureml-api-2
配置 DenseNet 图像分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/densenet?view=azureml-api-2
配置“编辑元数据”组件以调整列角色https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/edit-metadata?view=azureml-api-2
为小型数据集设置“手动输入数据”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/enter-data-manually?view=azureml-api-2
在 Azure ML 中配置“评估模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model?view=azureml-api-2
配置“评估推荐器”组件以评估模型准确性https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-recommender?view=azureml-api-2
在 Azure ML 设计器中配置“执行 Python 脚本”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-python-script?view=azureml-api-2
在 Azure ML 中配置“执行 R 脚本”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-r-script?view=azureml-api-2
配置“导出数据”组件以保存管道输出https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/export-data?view=azureml-api-2
在 Azure ML 中配置“从文本中提取 N 元语法特征”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/extract-n-gram-features-from-text?view=azureml-api-2
在 Azure ML 中配置快速森林分位数回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/fast-forest-quantile-regression?view=azureml-api-2
在 Azure ML 中配置特征哈希文本组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/feature-hashing?view=azureml-api-2
配置基于筛选器的特征选择以选择预测列https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection?view=azureml-api-2
在 Azure ML 设计器中使用图搜索查询语法https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/graph-search-syntax?view=azureml-api-2
配置“将数据分组到箱中”组件以进行离散化https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/group-data-into-bins?view=azureml-api-2
为 Azure ML 设计器管道配置“导入数据”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/import-data?view=azureml-api-2
在 Azure ML 设计器中配置“初始化图像转换”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/init-image-transformation?view=azureml-api-2
配置“联接数据”组件以合并数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/join-data?view=azureml-api-2
在 Azure ML 中配置 K-Means 聚类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering?view=azureml-api-2
在 Azure ML 中配置潜在狄利克雷分配组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/latent-dirichlet-allocation?view=azureml-api-2
在 Azure ML 中配置线性回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression?view=azureml-api-2
在 Azure ML 中配置多类提升决策树https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-boosted-decision-tree?view=azureml-api-2
在 Azure ML 中配置多类决策森林https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-decision-forest?view=azureml-api-2
在 Azure ML 中配置多类逻辑回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-logistic-regression?view=azureml-api-2
在 Azure ML 中配置多类神经网络https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-neural-network?view=azureml-api-2
在 Azure ML 中设置神经网络回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/neural-network-regression?view=azureml-api-2
配置“规范化数据”组件以缩放特征https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/normalize-data?view=azureml-api-2
在 Azure ML 中配置“一对多多类”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-all-multiclass?view=azureml-api-2
在 Azure ML 中配置“一对一多类”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-one-multiclass?view=azureml-api-2
配置“分区和采样”组件以拆分数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/partition-and-sample?view=azureml-api-2
配置已弃用的基于 PCA 的异常检测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/pca-based-anomaly-detection?view=azureml-api-2
配置“排列特征重要性”组件以分析模型https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/permutation-feature-importance?view=azureml-api-2
在 Azure ML 设计器中使用泊松回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/poisson-regression?view=azureml-api-2
在 Azure ML 中配置“预处理文本”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/preprocess-text?view=azureml-api-2
配置“删除重复行”组件以进行去重https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/remove-duplicate-rows?view=azureml-api-2
在 Azure ML 中配置 ResNet 图像分类https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/resnet?view=azureml-api-2
在 Azure ML 中配置“评分图像模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-image-model?view=azureml-api-2
在 Azure ML 设计器中配置“评分模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-model?view=azureml-api-2
配置“评分 SVD 推荐器”以生成预测https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-svd-recommender?view=azureml-api-2
在 Azure ML 中配置“评分 Vowpal Wabbit 模型”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-vowpal-wabbit-model?view=azureml-api-2
配置“评分 Wide & Deep 推荐器”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-wide-and-deep-recommender?view=azureml-api-2
配置“选择数据集中的列”以筛选特征https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-in-dataset?view=azureml-api-2
配置“选择列转换”以获得稳定的特征集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-transform?view=azureml-api-2
配置 SMOTE 组件以对少数类进行过采样https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/smote?view=azureml-api-2
配置“拆分数据”组件以划分训练集和测试集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data?view=azureml-api-2
为数据集配置“拆分图像目录”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-image-directory?view=azureml-api-2
配置“汇总数据”组件以计算描述性统计信息https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/summarize-data?view=azureml-api-2
配置“训练异常检测模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-anomaly-detection-model?view=azureml-api-2
在 Azure ML 中配置“训练聚类模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model?view=azureml-api-2
在 Azure ML 中配置“训练 PyTorch 模型”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-pytorch-model?view=azureml-api-2
在 Azure ML 设计器中配置“训练 SVD 推荐器”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-svd-recommender?view=azureml-api-2
在 Azure ML 中配置“训练 Vowpal Wabbit 模型”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-vowpal-wabbit-model?view=azureml-api-2
配置“训练 Wide & Deep 推荐器”组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-wide-and-deep-recommender?view=azureml-api-2
在 Azure ML 中配置“调整模型超参数”https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/tune-model-hyperparameters?view=azureml-api-2
在 Azure ML 中配置双类平均感知器https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?view=azureml-api-2
在 Azure ML 中配置双类提升决策树https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-boosted-decision-tree?view=azureml-api-2
在 Azure ML 中配置双类决策森林https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-decision-forest?view=azureml-api-2
在 Azure ML 中配置双类逻辑回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-logistic-regression?view=azureml-api-2
在 Azure ML 中配置双类神经网络https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-neural-network?view=azureml-api-2
在 Azure ML 中配置双类 SVM 组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-support-vector-machine?view=azureml-api-2
配置 Web 服务输入和输出组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/web-service-input-output?view=azureml-api-2
为 Azure ML 终结点配置推理数据收集https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-collection?view=azureml-api-2
在 Azure ML SDK 和 CLI v2 作业中使用表达式https://learn.microsoft.com/en-us/azure/machine-learning/concept-expressions?view=azureml-api-2
为 Azure ML 在线部署指定模型https://learn.microsoft.com/en-us/azure/machine-learning/concept-online-deployment-model-specification?view=azureml-api-2
使用 Azure ML 预构建 Docker 映像进行推理https://learn.microsoft.com/en-us/azure/machine-learning/concept-prebuilt-docker-images-inference?view=azureml-api-2
配置和使用 Azure ML 负责任 AI 仪表板https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard?view=azureml-api-2
配置训练和推理的特征检索规范https://learn.microsoft.com/en-us/azure/machine-learning/feature-retrieval-concepts?view=azureml-api-2
在 Azure ML 中配置特征集物化https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-materialization-concepts?view=azureml-api-2
在交互式 ML 开发期间访问 Azure 云存储数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-interactive?view=azureml-api-2
为 Azure ML 配置 Kubernetes 计算目标https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
将 AKS 或 Arc Kubernetes 群集附加到 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace?view=azureml-api-2
配置 Azure AutoML 以进行时间序列预测https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast?view=azureml-api-2
在 Azure ML 中配置 AutoML 计算机视觉训练https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?view=azureml-api-2
配置 Azure AutoML 以进行自定义 NLP 训练https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models?view=azureml-api-2
为 Azure ML 在线终结点配置自动缩放https://learn.microsoft.com/en-us/azure/machine-learning/how-to-autoscale-endpoints?view=azureml-api-2
为 PyTorch 环境配置自定义 Azure 容器https://learn.microsoft.com/en-us/azure/machine-learning/how-to-azure-container-for-pytorch-environment?view=azureml-api-2
为 Azure ML 终结点启用生产推理数据收集https://learn.microsoft.com/en-us/azure/machine-learning/how-to-collect-production-data?view=azureml-api-2
使用 Python 配置 Azure ML AutoML 训练作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train?view=azureml-api-2
使用 Azure ML v2 API 维护网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-network-isolation-with-v2?view=azureml-api-2
为 Azure ML 工作区配置专用终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2
配置 Azure ML 与外部数据和服务的连接https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connection?view=azureml-api-2
创建和管理 Azure ML 计算群集https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?view=azureml-api-2
在 studio 中配置和管理 Azure ML 计算https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio?view=azureml-api-2
创建用于开发的 Azure ML 计算实例https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
在 Azure ML prompt flow 中配置和使用向量索引https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-vector-index?view=azureml-api-2
使用 ARM 模板创建 Azure ML 工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template?view=azureml-api-2
使用启动脚本自定义 Azure ML 计算实例https://learn.microsoft.com/en-us/azure/machine-learning/how-to-customize-compute-instance?view=azureml-api-2
配置和使用 Azure ML 数据存储以访问存储https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
配置 Azure ML Kubernetes 扩展设置https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-kubernetes-extension?view=azureml-api-2
在虚拟网络中启用 Azure ML studio 访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-enable-studio-virtual-network?view=azureml-api-2
将外部数据导入 Azure ML（预览版）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-import-data-assets?view=azureml-api-2
在 Azure ML 中将 MLflow 模型记录为一等模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2
将 Azure ML 分布式训练日志发送到 Application Insightshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-search?view=azureml-api-2
在 Azure ML 中配置模型可解释性https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability?view=azureml-api-2
管理 Azure ML 计算实例及其生命周期https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-instance?view=azureml-api-2
使用 CLI 和 SDK 配置 Azure ML 环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
使用 Bicep 模板创建 Azure ML 中心工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-hub-workspace-template?view=azureml-api-2
配置和管理 Azure ML Kubernetes 实例类型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-kubernetes-instance-types?view=azureml-api-2
配置 Azure ML 模型部署模板https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-deployment-templates?view=azureml-api-2
使用 MLflow 管理 Azure ML 模型注册表https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-mlflow?view=azureml-api-2
使用 Azure ML CLI 和 SDK 注册和管理模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models?view=azureml-api-2
创建和管理 Azure ML 注册表https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2
在 Azure ML 中附加和管理 Synapse Spark 池https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool?view=azureml-api-2
使用 Terraform 预配 Azure ML 工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-terraform?view=azureml-api-2
在 Azure Machine Learning 中定义和使用 MLTable 架构https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2
收集和监视 Kubernetes 终结点推理日志https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-kubernetes-online-enpoint-inference-server-log?view=azureml-api-2
配置 Azure ML 生产环境中的模型性能监视https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
配置 Azure ML 在线终结点的监视和日志记录https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
在 Azure ML 计算实例上使用 R 和 RStudiohttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-r-interactive-development?view=azureml-api-2
为 Azure ML 注册表配置网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/how-to-registry-network-isolation?view=azureml-api-2
在 Azure ML 中使用负责任 AI 仪表板工具https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2
在 Azure ML studio 中生成负责任 AI 见解https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-ui?view=azureml-api-2
在 Azure ML 中配置和导出负责任 AI 评分卡https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard?view=azureml-api-2
在 Azure ML 中安排定期数据导入https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-data-import?view=azureml-api-2
在 Azure ML 工作区之间共享模型和组件https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2
在 Azure ML 中查询和比较 MLflow 实验及运行https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow?view=azureml-api-2
将 MLflow Projects 训练作业提交到 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-mlflow-projects?view=azureml-api-2
配置和提交 Azure ML 训练作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
配置和提交 Azure ML 训练作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
配置 Azure ML 超参数扫描作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
为 Azure ML 批处理配置低优先级 VM 和 Spot VMhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch?view=azureml-api-2
使用 MLflow 跟踪 Azure ML 实验和运行https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs?view=azureml-api-2
配置 MLflow 跟踪与 Azure Machine Learning 工作区的集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2
在 Azure ML 管道中配置和运行并行作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2
在 Azure ML 无服务器计算上运行训练作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2
在 Azure ML 管道中配置超参数扫描https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-sweep-in-pipeline?view=azureml-api-2
查看和标记 Azure ML 托管在线终结点的成本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-view-online-endpoints-costs?view=azureml-api-2
配置 VS Code 到 Azure ML 计算的远程会话https://learn.microsoft.com/en-us/azure/machine-learning/how-to-work-in-vs-code-remote?view=azureml-api-2
为 Azure ML 笔记本配置无服务器 Spark 计算https://learn.microsoft.com/en-us/azure/machine-learning/interactive-data-wrangling-with-apache-spark-azure-ml?view=azureml-api-2
Azure Machine Learning 监视指标参考https://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning-reference?view=azureml-api-2
大规模运行 Prompt Flow 批量评估https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-bulk-test-evaluate-flow?view=azureml-api-2
为 Azure ML prompt flow 自定义计算会话基础映像https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-session-base-image?view=azureml-api-2
创建和自定义评估流与指标https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2
为 prompt flow 终结点配置流式处理模式https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-streaming-mode?view=azureml-api-2
为 prompt flow 部署配置跟踪和反馈https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-trace-feedback-for-deployment?view=azureml-api-2
配置和管理 Prompt Flow 计算会话https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-manage-compute-session?view=azureml-api-2
为 Azure ML 中的生成式 AI 终结点配置监视https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications?view=azureml-api-2
通过 YAML 配置 Automated ML 预测作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automated-ml-forecasting?view=azureml-api-2
使用 YAML 编写 AutoML 图像分类作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-classification?view=azureml-api-2
定义 AutoML 图像实例分割 YAML 作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-instance-segmentation?view=azureml-api-2
配置 AutoML 图像多标签分类 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-multilabel-classification?view=azureml-api-2
使用 YAML 编写 AutoML 图像对象检测作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-object-detection?view=azureml-api-2
在 Azure ML 中配置 AutoML 视觉超参数https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters?view=azureml-api-2
为 AutoML 计算机视觉设置 JSONL 数据格式https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-schema?view=azureml-api-2
配置 AutoML 多标签文本分类 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-multilabel-classification?view=azureml-api-2
使用 YAML 架构编写 AutoML NLP NER 作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-ner?view=azureml-api-2
使用 YAML 定义 AutoML 文本分类作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-text-classification?view=azureml-api-2
Azure ML Kubernetes 配置参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-kubernetes?view=azureml-api-2
通过 Azure ML YAML 定义命令组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command?view=azureml-api-2
使用 Azure ML YAML 架构编写管道组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-pipeline?view=azureml-api-2
在 Azure ML YAML 架构中配置 Spark 组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-spark?view=azureml-api-2
通过 YAML 在 Azure ML 中配置 AmlCompute 群集https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-aml?view=azureml-api-2
使用 YAML 定义 Azure ML 计算实例https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-instance?view=azureml-api-2
在 Azure ML YAML 中配置附加的 Kubernetes 群集https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-kubernetes?view=azureml-api-2
使用 Azure ML YAML 架构附加和配置 VMhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-vm?view=azureml-api-2
在 AML YAML 中配置 AI Content Safety 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-content-safety?view=azureml-api-2
为 AML 编写 AI Search 连接 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-search?view=azureml-api-2
使用 Azure ML YAML 配置 Foundry Tools 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-services?view=azureml-api-2
通过 AML YAML 架构定义 API 密钥连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-api-key?view=azureml-api-2
通过 AML YAML 定义 Azure OpenAI 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-azure-openai?view=azureml-api-2
在 AML YAML 中定义 Blob 数据存储连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-blob?view=azureml-api-2
在 AML 中配置 Azure Container Registry 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-container-registry?view=azureml-api-2
在 Azure ML YAML 中编写自定义密钥连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-custom-key?view=azureml-api-2
通过 AML YAML 配置 Data Lake Gen2 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-data-lake?view=azureml-api-2
在 AML YAML 中配置 Git 存储库连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-git?view=azureml-api-2
使用 AML YAML 设置 OneLake 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-onelake?view=azureml-api-2
在 AML YAML 中配置 OpenAI 服务连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-openai?view=azureml-api-2
使用 AML YAML 设置 Python 源连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-python-feed?view=azureml-api-2
通过 Azure ML YAML 定义 Serp 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serp?view=azureml-api-2
为 Azure ML 编写无服务器连接 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serverless?view=azureml-api-2
在 AML YAML 中配置 AI Speech Services 连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-speech?view=azureml-api-2
了解 Azure ML CLI v2 YAML 核心语法https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-core-syntax?view=azureml-api-2
Azure ML 数据 YAML 架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-data?view=azureml-api-2
在 Azure ML 中通过 YAML 定义 Azure Blob 数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-blob?view=azureml-api-2
编写 Azure Data Lake Gen1 数据存储 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen1?view=azureml-api-2
在 YAML 中配置 Azure Data Lake Gen2 数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen2?view=azureml-api-2
使用 YAML 架构配置 Azure Files 数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-files?view=azureml-api-2
为 Azure ML 编写批处理部署 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-batch?view=azureml-api-2
在 Azure ML YAML 中定义 Kubernetes 在线部署https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-kubernetes-online?view=azureml-api-2
通过 Azure ML YAML 配置托管在线部署https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-managed-online?view=azureml-api-2
配置 Azure ML CLI v2 部署模板 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-template?view=azureml-api-2
为 Azure ML CLI v2 编写批处理终结点 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-batch?view=azureml-api-2
使用 YAML 配置 Azure ML 在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-online?view=azureml-api-2
Azure ML 环境 YAML 架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-environment?view=azureml-api-2
通过 Azure ML YAML 编写特征实体定义https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-entity?view=azureml-api-2
使用 Azure ML YAML 创建特征检索规范https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-retrieval-spec?view=azureml-api-2
在 Azure ML YAML 架构中配置特征集https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-set?view=azureml-api-2
使用 YAML 在 Azure ML 中定义特征存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-store?view=azureml-api-2
使用 YAML 架构定义特征集规范https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-featureset-spec?view=azureml-api-2
配置 Azure ML CLI v2 命令作业 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-command?view=azureml-api-2
配置 Azure ML 并行作业 YAML 架构https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel?view=azureml-api-2
使用 YAML 架构配置 Azure ML 管道作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
使用 YAML 在 Azure ML 中配置 Spark 作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-spark?view=azureml-api-2
使用 Azure ML YAML 定义扫描（超参数）作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-sweep?view=azureml-api-2
Azure ML MLTable YAML 架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable?view=azureml-api-2
使用 CLI v2 YAML 架构定义 Azure ML 模型https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-model?view=azureml-api-2
使用 Azure ML YAML 创建模型监视计划https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-monitor?view=azureml-api-2
浏览 Azure ML CLI v2 YAML 架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-overview?view=azureml-api-2
使用 CLI v2 YAML 定义 Azure ML 注册表https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-registry?view=azureml-api-2
为 Azure ML 编写数据导入计划 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule-data-import?view=azureml-api-2
使用 YAML 配置 Azure ML 作业计划https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule?view=azureml-api-2
Azure ML 工作区 YAML 架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-workspace?view=azureml-api-2
集成与编码模式
主题URL
将 Fabric OneLake 表复制到 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/create-datastore-with-user-interface?view=azureml-api-2
为 AML 批处理终结点作业配置输入数据源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
在 Azure ML Spark 作业中运行 MLflow 模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-model-spark-jobs?view=azureml-api-2
使用 Azure ML REST API 进行在线部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-rest?view=azureml-api-2
对 Azure AutoML 图像模型运行本地 ONNX 推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-onnx-automl-image-models?view=azureml-api-2
使用 Azure ML 推理 HTTP 服务器进行本地调试https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-server-http?view=azureml-api-2
使用 MLflow 在 Azure ML 中记录指标和项目https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2
使用 REST API 管理 Azure ML 资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-rest?view=azureml-api-2
通过 VNet 将 Azure Synapse 与 Azure ML 安全集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse?view=azureml-api-2
在 Azure ML 作业中读取和写入数据（v2 SDK）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
使用 Azure ML SDK 生成负责任 AI 仪表板https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-sdk-cli?view=azureml-api-2
通过专用终结点将受保护的 Azure Databricks 附加到 Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-securely-attach-databricks?view=azureml-api-2
在 Azure ML 中提交独立 Spark 作业和管道 Spark 作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-submit-spark-jobs?view=azureml-api-2
使用 Azure ML SDK v2 训练 PyTorch 模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch?view=azureml-api-2
在 .NET 应用中将 Azure AutoML ONNX 模型与 ML.NET 配合使用https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-onnx-model-dotnet?view=azureml-api-2
从 Azure Data Factory 调用 Azure ML 批处理终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-azure-data-factory?view=azureml-api-2
将 Fabric 与 Azure ML 批处理终结点集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-fabric?view=azureml-api-2
从 Event Grid 触发 Azure ML 批处理终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid-batch?view=azureml-api-2
将 Azure ML 事件与 Event Grid 工作流集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
将 Azure Databricks MLflow 跟踪与 Azure ML 集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2
配置从 Azure Synapse 到 Azure ML 的 MLflow 跟踪https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-synapse?view=azureml-api-2
在 Azure ML 中设置 RAG prompt flow 示例https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-retrieval-augmented-generation?view=azureml-api-2
在 prompt flow 中创建和使用自定义工具包https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2
将 LangChain 工作流集成到 Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-langchain?view=azureml-api-2
使用 Microsoft Agent Framework 重建 Prompt Flow 工作流https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-migrate-prompt-flow-to-agent-framework?view=azureml-api-2
在 Azure ML prompt flow 中处理和使用图像https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-process-image?view=azureml-api-2
在 prompt flow 中使用 Azure OpenAI GPT-4 Turbo with Vision 工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/azure-open-ai-gpt-4v-tool?view=azureml-api-2
在 prompt flow 中使用 Content Safety 文本工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/content-safety-text-tool?view=azureml-api-2
为 prompt flow RAG 配置嵌入工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/embedding-tool?view=azureml-api-2
为 prompt flow 中的向量搜索配置 Index Lookup 工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/index-lookup-tool?view=azureml-api-2
配置和使用 prompt flow 中的 LLM 工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/llm-tool?view=azureml-api-2
为开源模型配置 Open Model LLM 工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool?view=azureml-api-2
通过 prompt flow 工具集成 OpenAI GPT-4V 视觉模型https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/openai-gpt-4v-tool?view=azureml-api-2
在 prompt flow 中使用提示词工具模板https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/prompt-tool?view=azureml-api-2
在 prompt flow 中构建和配置 Python 工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/python-tool?view=azureml-api-2
为 prompt flow 搜索设置重排序工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/rerank-tool?view=azureml-api-2
通过 prompt flow 工具集成 SerpAPI 搜索结果https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/serp-api-tool?view=azureml-api-2
快速入门：在 Azure ML 中配置 Spark 作业https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-spark-jobs?view=azureml-api-2
将 Azure ML v1 日志记录 API 映射到 MLflow 跟踪https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking?view=azureml-api-2
部署
主题URL
跨工作区使用 Azure ML 标准部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-models-serverless?view=azureml-api-2
将 AutoML 模型部署到 AML 在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-automl-endpoint?view=azureml-api-2
将自定义容器模型部署到 AML 在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-custom-container?view=azureml-api-2
渐进式部署 MLflow 模型到 Azure ML 在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2
将 MLflow 模型部署到 Azure ML 终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models?view=azureml-api-2
自定义 Azure ML 批处理部署输出https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-custom-output?view=azureml-api-2
使用部署模板部署 Azure ML 注册表模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-deployment-template?view=azureml-api-2
将 Hugging Face 模型部署到 Azure ML 终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?view=azureml-api-2
将目录模型部署为标准无服务器终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-serverless?view=azureml-api-2
在 Azure ML 上使用 Triton 部署 ONNX 模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?view=azureml-api-2
为 Azure ML 训练创建 GitHub Actions CI/CDhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?view=azureml-api-2
使用 AML 批处理终结点部署图像处理模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-image-processing-batch?view=azureml-api-2
将 MLflow 模型部署到 Azure ML 批处理终结点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?view=azureml-api-2
使用 VS Code 在本地运行 Azure ML RAG 提示词流https://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrieval-augmented-generation-cloud-to-local?view=azureml-api-2
为 Azure ML 在线终结点执行安全的蓝绿发布https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2
使用 Azure DevOps 和 Azure ML 设置端到端 MLOpshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml?view=azureml-api-2
使用 GitHub 和 Azure ML 设置端到端 MLOpshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-github-azure-ml?view=azureml-api-2
通过 AML 批处理终结点运行 Azure OpenAI 嵌入https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-openai-embeddings?view=azureml-api-2
通过 AML 批处理终结点部署和调用管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-deployments?view=azureml-api-2
将现有 AML 管道作业转换为批处理终结点部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-from-job?view=azureml-api-2
在 AML 批处理终结点上实现评分管道的运营化https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-scoring-pipeline?view=azureml-api-2
在 AML 批处理终结点上实现训练管道的运营化https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-training-pipeline?view=azureml-api-2
使用 Azure ML 和 prompt flow 构建 RAG 管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipelines-prompt-flow?view=azureml-api-2
在 Azure ML 中设置和部署 prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/get-started-prompt-flow?view=azureml-api-2
将迁移后的 Agent Framework 工作流部署到 Azurehttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-migrated-agent-framework-workflow?view=azureml-api-2
将 prompt flow 部署到 Azure ML 在线终结点https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-to-code?view=azureml-api-2
使用 prompt flow 和 Azure DevOps 设置 GenAIOps 管道https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-azure-devops-with-prompt-flow?view=azureml-api-2

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