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href=\"https://www.snowflake.com/en/blog/fuel-ai-strategy-with-connected-accessible-data/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eデータファウンデーションの準備\u003C/a\u003Eと\u003Ca href=\"https://www.snowflake.com/en/blog/integrated-developer-environment-ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E開発者ワークフローのモダナイゼーション\u003C/a\u003EによってAIの準備を加速する新機能に加え、企業がAIを大規模に使用および展開する方法を簡素化するイノベーションを発表できることを嬉しく思います。これらの機能を使用して、以下を実現できます。 \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESnowflake Intelligenceにより、すべてのユーザーが自然言語で複雑な質問に回答\u003C/b\u003Eできる \u003C/li\u003E\n\u003Cli\u003Eすべての構造化データと非構造化データを含むエンタープライズコンテキストデータベース上で、推論の強化とすぐに使える主要なモデルプロバイダーへのアクセスにより、直感的かつ\u003Cb\u003E柔軟にデータエージェントを開発\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E組み込みのコストガバナンス制御など、シンプルなSQLを使用して\u003Cb\u003EマルチモーダルAIパイプラインを加速\u003C/b\u003Eし、TCO管理を容易にする\u003C/li\u003E\n\u003Cli\u003E高度なMLOps機能と、オンライン予測のための低レイテンシーのML機能によって\u003Cb\u003E実稼働MLを加速\u003C/b\u003E\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003ESnowflake Intelligence：すべての知識。信頼できる唯一のエンタープライズエージェント。 \u003C/h2\u003E\n\u003Cp\u003Eあまりにも長い間、インサイトが静的なダッシュボードや硬直したレポートに埋もれており、構築に時間がかかり、ナビゲートが難しく、回答よりも多くの疑問が投げかけられることがよくありました。\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-intelligence-enterprise-ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Intelligence\u003C/a\u003E \u003C/b\u003E(一般提供中)は、AIの新しいカテゴリであるエンタープライズインテリジェンスエージェントを導入しています。このエージェントは、真のデータドリブンな文化を醸成することで、組織の働き方、コラボレーション、イノベーションを変革します。すべての従業員は、技術的詳細に関係なく、Snowflakeのセキュアでガバナンスの確保された領域内で、自然言語で複雑な質問を行い、「なぜ」という背後にある「なぜ」を明らかにし、自信を持ってアクションを実行できます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-247fd60432","lazyEnabled":true,"alt":"tyler","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--46d2476f-931a-4713-8f0e-b203f527bc89/snowflake-intelligence-hometochat--intelligent-governed-ai.gif?preferwebp=true&quality=85","width":"1920","height":"1080","title":"Figure 1: Snowflake Intelligence is an enterprise intelligence agent that makes organizational insights accessible to every employee through natural language.",":type":"snowflake-site/components/image"},"blog_text_1335903993":{"id":"blog-text-84a693756e","text":"\u003Cp\u003ESnowflake Intelligenceは、データとインサイトを民主化し、誰もが自信を持って推論、意思決定、行動できるようにします。ユーザーは、信頼できる単一の環境で、すべてのデータ、ビジネスコンテキスト、豊富なセマンティクスから分析、推論、検証済みの回答を得られます。 \u003C/p\u003E\n\u003Cp\u003Eお客様は、\u003Cb\u003EAgentic Document Analytics\u003C/b\u003E（近日中にプライベートプレビュー）を使用して、非構造化データを含むすべてのデータの回答にSnowflake Intelligenceを使用できます。ユーザーは、1回のクエリで数十個のドキュメントをサンプリングする従来のRAGに制限されなくなりました。しかし、1回のクエリで数千個のドキュメントを分析できます。ドキュメントに対して強力な質問ができるようになったため、「パスワードをリセットするには？」から「過去6か月間のカスタマーサポートチケットの製品領域別の週次メンション数を表示する」に変更できます。\u003C/p\u003E\n\u003Cp\u003Eまた、Snowflake Intelligenceは、\u003Cb\u003Eセマンティックレイヤーをオートパイロット\u003C/b\u003E（近日中にパブリックプレビュー）にします。つまり、ビジネスの進化に合わせて適応し、より迅速にインサイトを得られるようになります。ライブ使用状況データとクエリ履歴から学習することで、検証済みの新しいクエリを自動的に提示してレビューし、メトリクス、定義、ビジネスロジックの理解を継続的に改善します。Snowflakeは、Tableauなどのツール、手動で入力したSQL、その他のBIプラットフォームからコンテキストを自動的にインポートできます。近日中に、セマンティックモデルを迅速かつ継続的に改善できるようになります。これにより、1日目からより高品質なモデル、価値実現までの時間の短縮、信頼できるインサイトを得られます。 \u003C/p\u003E\n\u003Cp\u003ESnowflake Intelligenceは、信頼できる単一のエンタープライズエージェントですべての知識を提供することで、質問とインサイトの間の障壁を取り除き、従業員が協力して探索、推論、イノベーションを行えるようにします。 \u003C/p\u003E\n\u003Ch2\u003E信頼性の高いコンテキストリッチなデータエージェントの構築 \u003C/h2\u003E\n\u003Cp\u003ESnowflakeは、信頼性の高い実稼働可能なデータエージェントを構築するための強力なツールを提供し、すべての構造化データと非構造化データをより正確に取得できます。これらのエージェントは、Snowflake Intelligenceの事前構築された自然言語インターフェイスから簡単にアクセスすることも、Snowflake \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Agents AP\u003C/a\u003E \u003C/b\u003EI（一般提供中）を使用してカスタムのマルチエージェントアーキテクチャに統合することもできます。\u003C/p\u003E\n\u003Cp\u003ECortex Agentsは、構造化データと非構造化データのソースをオーケストレーションを実施し、インサイトを提供します。Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Search\u003C/a\u003E、Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Analyst\u003C/a\u003Eを使用して、複雑なクエリを分解し、関連データを取り出し、正確な回答を生成します。オーケストレーションは、OpenAI GPTやAnthropic Claudeなど、すぐに使用できるLLMから選択できます。これにより、あらゆる段階で正確性、効率性、ガバナンスが確保されます。\u003C/p\u003E\n\u003Cp\u003ESnowflakeは、ドメイン固有のインサイトやコンテキスト認識のインサイトを提供するエージェントを簡単に構築できるようにします。Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-knowledge-extensions/cke-overview\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003ECortex Knowledge Extensions\u003C/b\u003E\u003C/a\u003E\u003Cb\u003E\u003C/b\u003Eと\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/sharing-semantic-views\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003Eセマンティックビューの共有\u003C/b\u003E\u003C/a\u003E（いずれも一般提供中）により、企業はサードパーティプロバイダーやパブリッシャーからのコンテキストや知識を使用してエージェントインタラクションをエンリッチし、同時にコンテンツ所有者の知的財産の保護と適切な帰属を実現できます。\u003Ca href=\"https://app.snowflake.com/marketplace/data-products?sortBy=date_desc&categorySecondary=%5B%2227%22%5D\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflakeマーケットプレイス\u003C/a\u003Eで利用可能な最新のAI対応データプロバイダーとパブリッシャーには、Alation、Allium、The Associated Press、CARTO、CB Insights、Cotality、Crisp、Crunchbase、Deutsche Börse、Equilar、FactSet、Flipside Crypto、Investopedia、IPinfo、LSEG、MSCI、NASDAQ、Nordot、Truestar、The Washington Postなどがあります。さらに、Snowflakeは、Snowflakeマーケットプレイスで、\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV65X/snowflake-public-data-products-snowflake-public-data-cortex-knowledge-extensions\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E決算報告\u003C/a\u003E、\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9ORD/snowflake-clinical-trials-research-database\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E臨床試験\u003C/a\u003E、\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9OQW/snowflake-pubmed-biomedical-research-corpus\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E医学研究\u003C/a\u003E、\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9OQ4/snowflake-snowflake-documentation\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflakeドキュメント\u003C/a\u003Eなど、AIに対応したさまざまな公開データソースを提供しています。\u003C/p\u003E\n\u003Cp\u003ESnowflakeでエージェントやアプリケーションを構築する際には、相互運用可能で他のシステムに接続されていることを確認する必要があります。MCPは、エージェントツールの接続と統合のための標準的な方法となっています。\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003ESnowflakeマネージドMCPサーバー\u003C/b\u003E\u003C/a\u003E\u003Cb\u003E\u003C/b\u003E（一般提供中）では、MCPプロトコルを使用してCortex Agent、Cortex Analyst、Cortex Searchにセキュアに接続できます。これにより、他のシステムやマルチエージェントアーキテクチャは、Snowflakeのセキュアなガバナンスの境界を出ることなく、Snowflake AIを通じて高品質なビジネスコンテキストを取得できます。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ESnowflake Interactive Tables and Warehouse\u003C/b\u003E（近日中に一般提供）は、実稼働グレードの高性能AIワークロードをサポートするために、エージェントドリブンなエクスペリエンスの強化に必要な1秒未満のサービングレイヤーを提供します。 \u003C/p\u003E\n\u003Ch2\u003Eより高速なマルチモーダルAIパイプラインの構築 \u003C/h2\u003E\n\u003Cp\u003ESnowflakeは、 \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/aisql?lang=de%2F#available-functions\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex AISQ\u003C/a\u003E \u003C/b\u003EL（一般提供中）によるデータ分析を再考し、使い慣れたSQLを使用してテキスト、ドキュメント、画像、音声、動画を扱うAIパイプラインの構築を可能にします。個別のAIサービスは不要です。開発者は、AI_COMPLETE、AI_TRANSCRIBE、AI_CLASSIFYなどの大規模なマルチモーダルデータ分析のための実稼働可能な関数から、AI_PARSE_DOC、AI_EXTRACTなどのドキュメント固有の関数まで、あらゆる規模の複雑なユースケースに対応できます。ユーザーは、直感的な\u003Cb\u003EDocument AI Playground\u003C/b\u003E（パブリックプレビュー中）にファイルを直接アップロードして、ノーコードで体験できます。情報の抽出や解析を行い、対応するCortex AISQLコードを自動生成できます。Cortex AISQLのダイナミックテーブルのサポートにより、増分更新を通じてAIドリブンなインサイトを継続的に最新の状態に保つことができます。\u003C/p\u003E\n\u003Cp\u003ECortex AISQLは、すべてのSQLユーザーがAI処理にアクセスできるようにするとともに、AIパイプラインを簡単に構築できるようにします。これにより、開発プロセスが合理化され、提供が迅速化し、開発者体験が向上します。さらに、高パフォーマンスのAIパイプラインを提供します。類似のサイズのモデルを使用した概念実証（PoC）コードに基づくCortex AISQLランタイムの結果は、Snowflakeのエンジンに直接組み込まれたパフォーマンス最適化の結果、手動パイプラインと比較して\u003Ca href=\"https://www.snowflake.com/en/blog/ai-sql-query-language/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E3～7倍のクエリランタイム高速化\u003C/a\u003Eを示しています。このパフォーマンスの向上は、コンピュートコストの削減と価値実現までの時間の短縮につながります。 \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_472436161":{"id":"image-820264d8af","lazyEnabled":true,"alt":"2","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--21ab4922-e827-44cb-91f4-19a657d6c10e/figure-2-intelligent-governed-ai.png?preferwebp=true&quality=85","width":"994","height":"660","title":"Figure 2: POC: Cortex AISQL improves query speed by three to seven times. Manual AI pipeline runtime results are based on proof-of-concept code using similarly sized models as the AISQL counterpart.",":type":"snowflake-site/components/image"},"blog_text_1142948434":{"id":"blog-text-b7b8c142de","text":"\u003Cp\u003E総保有コストを簡素化するために、ユーザーはAIトークンをカウントするヘルパー関数（パブリックプレビュー中）、機能レベルとモデルレベルのロールベースのアクセス制御（パブリックプレビュー中）、コストガードレール（近日中に一般提供）などの組み込みのコストガバナンス制御を利用できます。これらの機能により、エンタープライズグレードのガバナンスを維持しながら、コストの見積もり、モニタリング、管理を簡単に行えるようになります。 \u003C/p\u003E\n\u003Cp\u003Eその他の強化点としては、大規模な\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/cortex-aisql-query-optimization/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eクエリパフォーマンス最適化\u003C/a\u003E、データ型サポートの拡張、データ変換の簡素化のための\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/new-sql-features-public-preview/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E新しいSQL構造と関数\u003C/a\u003Eなどがあります。組み込みのパフォーマンスの可観測性により、パイプラインは大規模でも効率的で費用対効果の高い状態を維持できます。 \u003C/p\u003E\n\u003Cp\u003Eお客様は、Cortex AISQLを使用して強力なマルチモーダルAIパイプラインを構築し、より高速なパフォーマンスと低コストを実現しています。これには、従業員とビジネスソリューションのグローバルリーダーであるAllegis Groupや、160か国以上で事業を展開する電力管理企業であるEatonなどの顧客が含まれます。 \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-db6ccff727","alignment":"left","layout":"card","quoteSourceName":" Jack Vorsteg","quoteSourceTitle":"Director of Enterprise Data & Integration at Allegis Group","showQuoteIcon":false,"quote":"SnowflakeのドキュメントAI機能は、当社の新しい契約ライフサイクル管理（Contract Lifecycle Management）プラットフォームへの移行において、非常に貴重なものとなっています。すでにPDF、docx、TIFFファイルを含む15万件以上のドキュメントを処理し、何万時間もの手作業（の時間）を削減しています。","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"quote_item_885518376":{"id":"quote-item-911d965468","alignment":"left","layout":"card","quoteSourceName":"Ross Schlamo","quoteSourceTitle":"Chief Data Officer at Eaton","showQuoteIcon":false,"quote":"サポート部門で使用する当社の顧客感情分析ツールでは、当初、Databricksのようなツールを使ったカスタムソリューションを開発し、通話ログをレビューしていました。Snowflake AI SQLに切り替えたところ、わずか数日で実用的なプロトタイプを提供できるようになり、処理時間は900%改善、カスタムビルド（独自開発）と比較して年間50万ドルのコスト削減も実現しました。顧客体験（カスタマーエクスペリエンス）の向上を実感しており、現在は、音声ファイルやその他のマルチモーダルデータにもAI SQLを活用し、感情分析能力をさらに強化することを計画しています。","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1163224754":{"id":"blog-text-872f5130f6","text":"\u003Ch2\u003E実稼働環境でのカスタムAI/MLモデルの構築と展開\u003C/h2\u003E\n\u003Cp\u003E\u003Ca href=\"http://www.snowflake.com/ml\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003Eは、ガバナンスの確保されたエンタープライズデータ上で直接構築される、開発、推論、運用のための完全に統合された一連の機能です。Snowflakeの最新の強化により、データサイエンティストやMLエンジニアは、データ出力なしにシングルプラットフォーム上で実稼働可能なモデルの構築、展開、管理をさらに簡単に行えるようになります。\u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/video/coinbase/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECoinbase\u003C/a\u003E、\u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/igs-energy/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EIGS Energy\u003C/a\u003E、\u003Ca href=\"https://www.youtube.com/watch?v=fJ2CIFZ_gDY\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EScene+\u003C/a\u003Eなどのお客様は、Snowflakeでより高速でスケーラブルなMLワークフローを構築しています。\u003C/p\u003E\n\u003Cp\u003Eモデル開発において、MLチームは、ネイティブに統合された\u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-ml/experiments\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003E実験追跡\u003C/b\u003E\u003C/a\u003E（パブリックプレビュー中）を通じて、トレーニング実行全体にわたって最も優れたパフォーマンスのモデルを特定、共有、再現することで、より簡単に高品質なモデルを構築できます。これにより、コラボレーションが簡素化され、再現性が向上し、企業全体にわたってモデルの反復が加速します。 \u003C/p\u003E\n\u003Cp\u003ESnowflake MLは、\u003Ca href=\"https://www.youtube.com/watch?v=IerdUQNpYIo&embeds_referring_euri=https%3A%2F%2Fdocs.google.com%2F&embeds_referring_origin=https%3A%2F%2Fdocs.google.com&source_ve_path=Mjg2NjY\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eモジュラー型MLワークフローをフルサポート\u003C/a\u003Eしており、外部MLプラットフォームやHugging Faceなどのモデルハブなど、あらゆる場所で構築されたモデルに対して、Snowflakeのガバナンスとセキュリティの制御を維持しながらスケーラブルな推論を実行できます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_66602520":{"id":"image-088d02d0ae","lazyEnabled":true,"alt":"3","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d6eafaf7-a65e-49b8-a089-aec69a76f3b3/figure-3--intelligent-governed-aipng.png?preferwebp=true&quality=85","width":"1972","height":"882","title":"Figure 3: Snowflake ML supports scalable inference with models built anywhere. ",":type":"snowflake-site/components/image"},"blog_text_1059883567":{"id":"blog-text-e7a631f95c","text":"\u003Cp\u003E推論では、シンプルなUIから \u003Cb\u003Eワンクリック展開 \u003C/b\u003E（パブリックプレビュー中）で事前トレーニング済みの \u003Cb\u003Eハギングフェイスモデル\u003C/b\u003E にほぼ瞬時にアクセスし、 \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/create-and-serve-online-features-python\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eオンライン特徴量ストア\u003C/a\u003E \u003C/b\u003E（パブリックプレビュー中）で50ミリ秒未満の低レイテンシー予測のための機能を提供できます。これにより、パーソナライズされたレコメンデーション、不正検知、料金最適化、異常検知などの重要なユースケースがサポートされ、利用可能な最新のデータに基づいてモデルが意思決定を行えるようになります。\u003C/p\u003E\n\u003Ch2\u003E詳細と今すぐお試しください\u003C/h2\u003E\n\u003Cp\u003Eデータ戦略はAI戦略に不可欠です。SnowflakeのAIの最新の進歩により、すべての企業が実稼働環境でAIの構築、展開、スケーリングを簡素化し、迅速化することで、測定可能なインパクトとビジネス成果の向上がもたらされます。 \u003C/p\u003E\n\u003Cp\u003E以下のステップバイステップのクイックスタートに従って、SnowflakeでのAI構築を今すぐ始めましょう。 \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-intelligence/index.html?index=..%2F..index#0\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Intelligenceを使用したエンタープライズインテリジェンスエージェントの構築\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/gain_insights_from_unstructured_data/index.html?index=..%2F..index#0\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex AISQLで非構造化データを大規模に処理\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/end-to-end-ml-workflow/index.html?index=..%2F..index#1\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake MLでエンドツーエンドモデルを生成する\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E将来の見通しに関する記述\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003Eこのページには、Snowflakeが将来提供する製品に関する記述を含め、将来の見通しに関する記述が含まれていますが、これはいかなる製品の提供も約束するものではありません。実際の成果や提供物は異なる可能性があり、既知および未知のリスクおよび不確実性の影響を受けます。詳細については、最新の四半期報告書（10-Q）をご覧ください。\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1335903993","image_472436161","blog_text_1142948434","quote_item","quote_item_885518376","blog_text_1163224754","image_66602520","blog_text_1059883567"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"premium_content_bann":"aem-GridColumn 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"}},":itemsOrder":["button"]},":type":"snowflake-site/components/premium-content-banner"}},":itemsOrder":["premium_content_bann"],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-240b81eb2e",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"author_chip":{"id":"author-chip-414a7cc7ee","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-21c8e8e519","lazyEnabled":true,"alt":"Lucy Zhu","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a8252970-74b8-459d-8d57-89f09e3a0766/lucy.jpg?preferwebp=true&quality=85","width":"438","height":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-055686b448","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/lucy-zhu/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Lucy Zhu"},"authorTitle":"Product Marketing Manager"},{"authorImage":{"id":"image-f78d66e4bc","lazyEnabled":true,"alt":"Baris Gultekin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3355b0f8-99db-4491-a7ae-7d4dc6d36aa8/official-baris-headshot-cropped-snowflake-wall-2.png?preferwebp=true&quality=85","width":"288","height":"288",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-d4fb488dc2","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/baris-gultekin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Baris Gultekin"},"authorTitle":"Vice President of AI, Snowflake"},{"authorImage":{"id":"image-fadb1a86d7","lazyEnabled":true,"alt":"Arun Agarwal","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?preferwebp=true&quality=85","width":"720","height":"653",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e17872bc28","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Arun Agarwal"},"authorTitle":"AI Product Marketing, Snowflake"},{"authorImage":{"id":"image-f1ac14dd81","lazyEnabled":true,"alt":"Headshot-style photo of Siddharth Dwivedi","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--be5d59dc-5c8a-479f-94a2-38cd590a83f8/siddharth-dwivedi.jpg?preferwebp=true&quality=85","width":"200","height":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-87be85e989","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/siddharth-dwivedi/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Siddharth Dwivedi"},"authorTitle":"AI Product Marketing, Snowflake"}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"]}},":itemsOrder":["container_hero","responsivegrid_content","responsivegrid_premium_content_banner","container_author_chip"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-0c9e44358c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"blog_table_of_content":{"id":"blog-table-of-content-55a6cc02fb","tableOfContents":[],":type":"snowflake-site/components/blog/blog-table-of-content"}},":itemsOrder":["blog_table_of_content"]},"isActiveTOC":false,"isBlogPage":true,":type":"snowflake-site/components/flexible-column-container"},"related_content":{"id":"related-content-cf1369cc00","relatedContent":[{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"製品 & テクノロジー","tagColor":"#3F808A"}],"authors":[{"authorImage":{"id":"image-7d4759f406","lazyEnabled":true,"alt":"Headshot-style photo of Zaki Bajwa of Snowflake","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--70b3b1bc-1cc2-44f5-ad54-bf8800ea36b8/zaki-bajwa.jpg?preferwebp=true&quality=85","width":"800","height":"800",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-b2906d5f29","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/zaki-bajwa/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Zaki Bajwa"}},{"authorImage":{"id":"image-dae570be85","lazyEnabled":true,"alt":"Avatar headshot of Tehzeeb Versey-Patel of Snowflake","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8abfe0e1-f452-4f0c-85cb-2c21dbcc9bef/tehzeeb-versey-patel.png?preferwebp=true&quality=85","width":"400","height":"400",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-387a31c307","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/tehzeeb-versey-patel/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Tehzeeb Versey-Patel"}}],"publicationDate":"JUN 17, 2026","title":{"lines":["Snowflake Well-Architected Frameworkを使用したAI時代に向けたアーキテクチャの構築"],"type":"heading4",":type":"snowflake-site/components/title-v2"},"button":{"id":"button-30b6f50546","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/ja/blog/snowflake-well-architected-framework/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Well-Architected Frameworkを使用したAI時代に向けたアーキテクチャの構築"},"image":{"id":"image-29508a75bd","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--29618be2-248e-40b6-a0a1-d862a52a3db4/sf-snowflake.png?preferwebp=true&quality=85","width":"1680","height":"720",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/blog/card"},{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"製品 & 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