jpskill.com
🛠️ 開発・MCP コミュニティ

Azure AI Language 開発

azure-language-service

Azure AI Languageサービスを用いた開発において、トラブルシューティング、ベストプラクティス、アーキテクチャ設計、セキュリティ、デプロイなど、専門的な知識とガイダンスを提供するSkillです。

📜 元の英語説明(参考)

Expert knowledge for Azure AI Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building CLU apps, custom NER/classification, CQA/QnA, PII/key phrase extraction, or health/summarization APIs, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Speech (use azure-speech), Azure Translator (use azure-translator), Azure AI Immersive Reader (use azure-immersive-reader).

🇯🇵 日本人クリエイター向け解説

一言でいうと

Azure AI Languageサービスを用いた開発において、トラブルシューティング、ベストプラクティス、アーキテクチャ設計、セキュリティ、デプロイなど、専門的な知識とガイダンスを提供するSkillです。

※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。

⚡ おすすめ: コマンド1行でインストール(60秒)

下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。

🍎 Mac / 🐧 Linux 
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o azure-language-service.zip https://jpskill.com/download/5720.zip && unzip -o azure-language-service.zip && rm azure-language-service.zip
🪟 Windows (PowerShell) 
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/5720.zip -OutFile "$d\azure-language-service.zip"; Expand-Archive "$d\azure-language-service.zip" -DestinationPath $d -Force; ri "$d\azure-language-service.zip"

完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。

💾 手動でダウンロードしたい(コマンドが難しい人向け)
  1. 1. 下の青いボタンを押して azure-language-service.zip をダウンロード
  2. 2. ZIPファイルをダブルクリックで解凍 → azure-language-service フォルダができる
  3. 3. そのフォルダを C:\Users\あなたの名前\.claude\skills\(Win)または ~/.claude/skills/(Mac)へ移動
  4. 4. Claude Code を再起動
⬇ .zip でダウンロード(推奨) ⬇ .skill 形式(上級者用) 元のソース ↗

⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。

🎯 このSkillでできること

下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。

📦 インストール方法 (3ステップ)

  1. 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
  2. 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
  3. 3. 展開してできたフォルダを、ホームフォルダの .claude/skills/ に置く
    • · macOS / Linux: ~/.claude/skills/
    • · Windows: %USERPROFILE%\.claude\skills\

Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。

詳しい使い方ガイドを見る →
最終更新
2026-05-17
取得日時
2026-05-18
同梱ファイル
1

📖 Skill本文(日本語訳)

※ 原文(英語/中国語)を Gemini で日本語化したものです。Claude 自身は原文を読みます。誤訳がある場合は原文をご確認ください。

Azure AI Language スキル

このスキルは、Azure AI Language に関する専門的なガイダンスを提供します。トラブルシューティング、ベストプラクティス、意思決定、アーキテクチャと設計パターン、制限とクォータ、セキュリティ、構成、統合とコーディングパターン、デプロイについて網羅しています。ローカルのクイックリファレンスコンテンツとリモートドキュメント取得機能を組み合わせています。

このスキルの使用方法

エージェントへの重要事項: 以下のカテゴリインデックスを使用して、関連するセクションを見つけてください。行範囲が指定されているカテゴリ（例: L35-L120）の場合は、指定された行で read_file を使用してください。ファイルリンクが指定されているカテゴリ（例: [security.md](security.md)）の場合は、リンクされた参照ファイルで read_file を使用してください。

エージェントへの重要事項: metadata.generated_at が3ヶ月以上前の場合は、リポジトリから最新バージョンをプルするようユーザーに提案してください。mcp_microsoftdocs ツールが利用できない場合は、ユーザーにインストールを提案してください: インストールガイド

このスキルは、ドキュメントコンテンツを取得するためにネットワークアクセスを必要とします。

  • 推奨: クエリ文字列 from=learn-agent-skill を指定して mcp_microsoftdocs:microsoft_docs_fetch を使用してください。Markdown を返します。
  • フォールバック: クエリ文字列 from=learn-agent-skill&accept=text/markdown を指定して fetch_webpage を使用してください。Markdown を返します。

カテゴリインデックス

カテゴリ 行 説明
トラブルシューティング L37-L42 Azure AI Language のカスタムテキスト分類およびカスタム質問応答プロジェクトにおける一般的なエラー、低精度な結果、および構成の問題の診断と修正。
ベストプラクティス L43-L59 多言語処理、絵文字、スキーマ、自動ラベリングなど、CLU、カスタム NER、テキスト分類、および CQA プロジェクトの設計、ラベリング、評価に関するベストプラクティス。
意思決定 L60-L69 リージョンとリソースの選択、ライフサイクルポリシー、および LUIS、QnA Maker、Text Analytics、Language Studio から Azure Language および Microsoft Foundry への移行パスに関するガイダンス。
アーキテクチャと設計パターン L70-L76 CLU とカスタムテキスト分類のアーキテクチャに関するガイダンス: CLU とオーケストレーションワークフローの選択、およびリージョンバックアップ、冗長性、フェイルオーバー戦略の設計。
制限とクォータ L77-L95 Azure AI Language 機能（CLU、NER、PII、キーフレーズ、QnA）の制限、クォータ、言語/リージョンサポート。データサイズ、スループット、コンテナ、トレーニングジョブの制約を含む。
セキュリティ L96-L105 Azure AI Language のセキュリティ: 保存時の暗号化、顧客管理キー、RBAC、マネージドID、SASトークン、および CQA リソースのネットワーク分離/Private Link。
構成 L106-L136 Azure AI Language プロジェクトとコンテナの構成: CLU、カスタム NER、テキスト分類、PII、CQA、感情分析、要約、ヘルス、リソース、フォーマット、トレーニング、および評価。
統合とコーディングパターン L137-L167 Azure AI Language/CLU/Health/Summarization/CQA API および SDK の呼び出し方法（同期/非同期）、JSON 結果の解釈、ボット/Power Automate との統合、およびカスタム NER/分類の使用。
デプロイ L168-L177 Azure AI Language モデル（カスタム分類、NER、QnA、キーフレーズ、言語検出）をリージョン、コンテナ、AKS にデプロイして実行する方法、およびプロジェクト/リソースを移行する方法。

トラブルシューティング

トピック URL
カスタムテキスト分類の一般的な問題を解決する https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/faq
カスタム質問応答の一般的な問題をトラブルシューティングする https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting

ベストプラクティス

トピック URL
Language で多言語と絵文字のオフセットを処理する https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/multilingual-emoji-support
CLU の会話設計のベストプラクティスを適用する https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/best-practices
多言語 CLU プロジェクトを効果的に実装する https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/multiple-languages
効果的な CLU プロジェクトスキーマを設計する https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/build-schema
CLU トレーニングのために発話をタグ付けおよびラベル付けする https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances
CLU モデルの評価を解釈し、安定させる https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/view-model-evaluation
カスタム NER のデータ準備とスキーマ設計 https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/design-schema
カスタム NER トレーニングのためにデータを効果的にラベル付けする https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/tag-data
自動ラベリングを使用してカスタム NER アノテーションを加速する https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/use-autolabeling
テキスト分類のデータ準備とスキーマ設計 https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/design-schema
カスタムテキスト分類のためにデータを効果的にラベル付けする https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/tag-data
CQA プロジェクト品質のベストプラクティスを実装する https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/best-practices
CQA でプロジェクト作成のベストプラクティスを適用する https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/bes
📜 原文 SKILL.md(Claudeが読む英語/中国語)を展開

Azure AI Language Skill

This skill provides expert guidance for Azure AI Language. 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.

How to Use This Skill

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

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

This skill requires network access to fetch documentation content:

  • Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
  • Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

Category Lines Description
Troubleshooting L37-L42 Diagnosing and fixing common errors, low-accuracy results, and configuration issues in custom text classification and custom question answering projects in Azure AI Language.
Best Practices L43-L59 Best practices for designing, labeling, and evaluating CLU, custom NER, text classification, and CQA projects, including multilingual handling, emojis, schemas, and autolabeling.
Decision Making L60-L69 Guidance on choosing regions and resources, lifecycle policies, and migration paths from LUIS, QnA Maker, Text Analytics, and Language Studio to Azure Language and Microsoft Foundry
Architecture & Design Patterns L70-L76 Architectural guidance for CLU and custom text classification: choosing CLU vs orchestration workflows, and designing regional backup, redundancy, and failover strategies.
Limits & Quotas L77-L95 Limits, quotas, and language/region support for Azure AI Language features (CLU, NER, PII, key phrases, QnA), including data sizes, throughput, containers, and training job constraints.
Security L96-L105 Security for Azure AI Language: encryption at rest, customer-managed keys, RBAC, managed identities, SAS tokens, and network isolation/Private Link for CQA resources.
Configuration L106-L136 Configuring Azure AI Language projects and containers: CLU, custom NER, text classification, PII, CQA, sentiment, summarization, health, resources, formats, training, and evaluation.
Integrations & Coding Patterns L137-L167 How to call Azure AI Language/CLU/Health/Summarization/CQA APIs and SDKs (sync/async), interpret JSON results, integrate with bots/Power Automate, and use custom NER/classification.
Deployment L168-L177 How to deploy and run Azure AI Language models (custom classification, NER, QnA, key phrases, language detection) across regions, containers, AKS, and migrate projects/resources.

Troubleshooting

Topic URL
Resolve common issues in custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/faq
Troubleshoot common issues in custom question answering https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting

Best Practices

Topic URL
Handle multilingual and emoji offsets in Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/multilingual-emoji-support
Apply CLU conversational design best practices https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/best-practices
Implement multilingual CLU projects effectively https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/multiple-languages
Design effective CLU project schemas https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/build-schema
Tag and label utterances for CLU training https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances
Interpret and stabilize CLU model evaluations https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/view-model-evaluation
Prepare data and design schemas for custom NER https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/design-schema
Label data effectively for custom NER training https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/tag-data
Use autolabeling to accelerate custom NER annotation https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/use-autolabeling
Prepare data and design schemas for text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/design-schema
Label data effectively for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/tag-data
Implement best practices for CQA project quality https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/best-practices
Apply project authoring best practices in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/best-practices

Decision Making

Topic URL
Understand Azure Language model lifecycle policies https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/model-lifecycle
Choose Azure regions for Language service features https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/regional-support
Migrate Azure Language Studio projects to Microsoft Foundry https://learn.microsoft.com/en-us/azure/ai-services/language-service/migration-studio-to-foundry
Choose and manage Azure resources for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/azure-resources
Decide migration from LUIS and QnA Maker to Azure Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate
Migrate Text Analytics apps to Azure Language API https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate-language-service-latest

Architecture & Design Patterns

Topic URL
Choose CLU vs orchestration workflow architecture https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/app-architecture
Design CLU regional backup and failover https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/fail-over
Design regional fail-over for custom text classification solutions https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/fail-over

Limits & Quotas

Topic URL
Data size and rate limits for Azure Language features https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/data-limits
Train and manage CLU model jobs and limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/train-model
Apply CLU Docker container request limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/use-containers
Apply CLU data, region, and throughput limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/service-limits
Check language and region support for custom NER https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/language-support
Language support matrix for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/language-support
Review custom text classification data and rate limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/service-limits
Check language support for entity linking API https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/language-support
Check language support for key phrase extraction https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/language-support
Review language detection supported languages and codes https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/language-support
Review language support for Named Entity Recognition https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/language-support
Review orchestration workflow data and throughput limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/orchestration-workflow/service-limits
Apply PII container per-call character and document limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/use-containers
Check language support for Azure PII detection https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/language-support
Custom question answering limits and boundaries https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/limits

Security

Topic URL
Understand Language service data-at-rest encryption https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/encryption-data-at-rest
Apply Azure RBAC to Azure Language resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/role-based-access-control
Use managed identities for Language Blob access https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/managed-identities
Create SAS tokens for Language Blob access https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/shared-access-signatures
Configure data-at-rest encryption and CMK for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/encrypt-data-at-rest
Configure network isolation and Private Link for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/network-isolation

Configuration

Topic URL
Configure Azure resources for CLU fine-tuning https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/configure-azure-resources
Configure Azure Language service containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/configure-containers
Format data correctly for CLU projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/data-formats
Configure and use CLU None intent https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/none-intent
Use CLU prebuilt entity components https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/prebuilt-component-reference
Create custom NER projects and configure Azure resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/create-project
Configure and run Custom NER Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/use-containers
Use required data formats for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/concepts/data-formats
Set up resources and create custom text classification projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/create-project
Configure and run training jobs for text classification models https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/train-model
View and interpret evaluation metrics for text classification models https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/view-model-evaluation
Map NER entity types and tags across API versions https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/concepts/ga-preview-mapping
Configure NER skill parameters and inference options https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to/skill-parameters
Configure conversation PII detection for chats and transcripts https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/redact-conversation-pii
Configure native document PII redaction with Azure Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/redact-document-pii
Configure text PII detection and redaction in Azure Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/redact-text-pii
Understand and configure confidence scores in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/confidence-score
Enable diagnostics and run analytics for CQA projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/analytics
Customize default answer behavior in CQA projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/change-default-answer
Add and configure chitchat personas in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/chit-chat
Configure Azure resources and permissions for CQA fine-tuning https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/configure-azure-resources
Configure smart URL refresh for CQA projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/smart-url-refresh
Use supported markdown formats in CQA answers https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/reference/markdown-format
Run Sentiment Analysis Docker containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/how-to/use-containers
Run Summarization Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/use-containers
Configure Text Analytics for health containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/configure-containers
Run Text Analytics for health containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/use-containers

Integrations & Coding Patterns

Topic URL
Integrate Azure Language SDK and REST APIs https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/developer-guide
Use Azure Language features asynchronously https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/use-asynchronously
Call CLU prediction APIs and SDKs https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/call-api
Integrate CLU with Bot Framework SDK https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/tutorials/bot-framework
Start building custom NER models via Foundry or REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/quickstart
Send prediction requests to custom text classification deployments https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/call-api
Call the entity linking API with correct parameters https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/how-to/call-api
Call entity linking via SDKs and REST API https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/quickstart
Call the key phrase extraction API correctly https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/how-to/call-api
Use key phrase extraction via .NET client library https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/quickstart
Call language detection API and interpret results https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/how-to/call-api
Implement language detection using SDKs and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/quickstart
Call the NER API to extract named entities https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to-call
Use the NER client library to extract entities https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/quickstart
Use Authoring API for custom question answering automation https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/authoring
Call the prebuilt CQA API for ad-hoc answering https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/prebuilt
Call Sentiment and Opinion Mining APIs https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/how-to/call-api
Call Sentiment Analysis via SDK and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/quickstart
Call conversation summarization API for chats https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/conversation-summarization
Summarize native documents via API https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/document-summarization
Use extractive text summarization API https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/text-summarization
Use Azure Summarization via SDK and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/quickstart
Enable FHIR structuring in health API output https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/concepts/fhir
Interpret relation extraction JSON output https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/concepts/relation-extraction
Call Text Analytics for health API https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/call-api
Quickstart Text Analytics for health via SDK/REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/quickstart
Use Azure Language in Power Automate flows https://learn.microsoft.com/en-us/azure/ai-services/language-service/tutorials/power-automate

Deployment

Topic URL
Deploy custom language projects to multiple regions https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/custom-features/multi-region-deployment
Deploy custom text classification models for prediction https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/deploy-model
Run key phrase extraction in Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/how-to/use-containers
Deploy language detection with Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/how-to/use-containers
Deploy NER with Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to/use-containers
Move custom question answering projects between resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/migrate-knowledge-base
Deploy Language containers to Azure Kubernetes Service https://learn.microsoft.com/en-us/azure/ai-services/language-service/tutorials/use-kubernetes-service