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href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/build-your-first-agentic-ml-pipeline-with-natural-language-2026-05-28/\"\u003Eバーチャルハンズオンラボ\u003C/a\u003E でご確認ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003E1. 概要\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E は、Agentic ML の開発方法を変革しています。Agentic ML とは、自律的・推論ベースのシステムであり、開発者が ML パイプライン全体にわたるタスクの計画・実行にエージェントを活用できるようにするものです。このクイックスタートでは、Snowflake の AI ネイティブコーディングエージェントである \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-coco/\"\u003ECoCo\u003C/a\u003E （旧称Cortex Code）を使用し、数回のプロンプトだけで顧客生涯価値 (LTV) 予測モデルを構築・実行する方法を学びます。生データから本番予測まで、数週間ではなく数分で到達できます。CoCo は CLI として、また Snowflake の Web インターフェースである Snowsight から直接ご利用いただけます。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E重要:\u003C/strong\u003E CoCo は LLM を基盤としており、非決定論的です。生成されるコードはこのガイドに示されているものと異なる場合があります。次のステップに進む前に、必ず出力を確認し、結果が期待通りであることをご確認ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E学習内容\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E自然言語プロンプトを使用した現実的な合成 EC データの生成\u003C/li\u003E\u003Cli\u003E会話形式での探索的データ分析とフィーチャーエンジニアリングの実施\u003C/li\u003E\u003Cli\u003ESnowflake 内での複数の回帰モデルのトレーニングと比較\u003C/li\u003E\u003Cli\u003ESnowflake Model Registry へのメトリクス付きモデルのログ\u003C/li\u003E\u003Cli\u003ESnowflake Warehouse でのバッチ推論の実行\u003C/li\u003E\u003Cli\u003E（オプション）リアルタイム推論のための Snowpark Container Services (SPCS) への REST API としてのモデルデプロイ\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E構築するもの\u003C/h3\u003E\n","\u003Cp\u003E顧客 LTV 予測の完全なパイプライン：\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E合成 EC トランザクションデータセット（約 500 顧客、18 ヶ月間で約 100,000 件のトランザクション）\u003C/li\u003E\u003Cli\u003E顧客の今後 90 日間の総支出を予測する訓練済み回帰モデル\u003C/li\u003E\u003Cli\u003E評価メトリクス付きで Snowflake Model Registry に登録されたモデル\u003C/li\u003E\u003Cli\u003ESnowflake Warehouse 経由のバッチ推論予測\u003C/li\u003E\u003Cli\u003E（オプション）レイテンシプロファイリング付きの SPCS 上のリアルタイム推論 REST エンドポイント\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E前提条件\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake の 30 日間\u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003E無料トライアル\u003C/a\u003Eへのサインアップ。\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E ロール、またはデータベース・スキーマ・テーブル・モデルの作成権限を持つロール\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight\"\u003ECoCo in Snowsight\u003C/a\u003E（ローカルインストール不要）\u003C/li\u003E\u003Cli\u003E専用の Snowflake Warehouse\u003C/li\u003E\u003Cli\u003E（SPCS 利用時、オプション）Snowpark Container Services 用に設定されたコンピュートプール — \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup\"\u003E公式セットアップガイド\u003C/a\u003Eをご参照ください\u003C/li\u003E\u003Cli\u003E基本的な ML の概念（トレーニング、評価、推論）に関する知識\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo CLI をご利用ですか？\u003C/strong\u003E 同じプロンプトはどちらのインターフェースでも動作します。CLI 固有のセットアップとターミナル出力の例については、\u003Ca href=\"#cortex-code-cli-walkthrough\"\u003ECoCo CLI ウォークスルー\u003C/a\u003Eをご参照ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E2. セットアップ\u003C/h2\u003E\n","\u003Ch3\u003ECoCo in Snowsight\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECoCo\u003C/a\u003E は Snowflake に組み込まれた AI エージェントであり、データエンジニアリング・アナリティクス・ML・エージェント構築タスク向けに設計されています。Snowflake 環境内で自律的に動作し、RBAC・スキーマ・プラットフォームのベストプラクティスに関する深い知識を活用します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003Eサイドバーから「Projects」>「Workspaces」をクリックして Workspace Notebook を開き、「My Workspace」パネルで「+ Add new」>「Notebook」をクリックします。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003Eノートブックが読み込まれたら、Snowsight の右下隅にある CoCo を確認します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: CoCo は環境を認識しており、Workspace Notebook で使用すると、ノートブックが提供するすべてのツールにアクセスできるため、最良の結果が得られます。関連する場合、生成されたコードはノートブックに挿入され、自動的に実行されます。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003Eこれで、CoCo にプロンプトを入力して ML パイプラインの構築を開始する準備が整いました。\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E3. 合成データの生成\u003C/h2\u003E\n","\u003Cp\u003Eまず、CoCo を使用してデータベースオブジェクトを作成し、合成 EC トランザクションデータを生成します。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EGenerate realistic looking synthetic data in database COCO_DB and schema COCO_SCHEMA \n(create if it doesn't exist). Create a table ML_LTV_TRANSACTIONS\nwith ~100000 transactions from ~500 customers over an 18-month period. Include\nCUSTOMER_ID, TRANSACTION_TIME, AMOUNT, PRODUCT_CATEGORY, and CHANNEL. Make the\ndata realistic: customers should have varying purchase frequencies (some buy\nweekly, others monthly), amounts should vary by category (electronics $50-$2000,\ngroceries $10-$200, apparel $20-$500), and channels should be web, mobile, or\nin-store. About 10% of customers should be high-value (frequent buyers with\nhigher average spend).\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E生成される内容\u003C/h3\u003E\n","\u003Cp\u003ECoCo のチャットパネルにプロンプトを入力します。CoCo はリクエストを分析し、複数ステップのプランに分解します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/generate-synthetic-data-by-cortex-code.jpg?v=23a691c1\" alt=\"Generate synthetic data by CoCo in Snowsight\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECoCo はデータベースオブジェクトの作成とテーブルへのデータ投入のための SQL または Python コードを生成し、自動的に実行します。新しい Notebook セルにコードと結果が表示されます。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/show-basic-statistics-by-cortex-code.jpg?v=23a691c1\" alt=\"Basic statistics by CoCo in Snowsight\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: LLM によるテキスト生成の固有のランダム性により、結果はこのチュートリアルに示されているものと若干異なる場合があります。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003Eデータが正しく生成されたことを確認するには、Snowsight ワークシートで以下の SQL を実行してください。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: \u003Ccode\u003ECOCO_DB.COCO_SCHEMA\u003C/code\u003E はデータベースとスキーマの例です。ご利用の環境で CoCo が別のデータベースまたはスキーマにデータを保存した場合は、クエリを実行する前にこれらの値を更新してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003ECUSTOMER_ID\u003C/code\u003E、\u003Ccode\u003ETRANSACTION_TIME\u003C/code\u003E、\u003Ccode\u003EAMOUNT\u003C/code\u003E、\u003Ccode\u003EPRODUCT_CATEGORY\u003C/code\u003E、\u003Ccode\u003ECHANNEL\u003C/code\u003E などのカラムを持つ 10 行が表示されるはずです。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E代替手段:\u003C/strong\u003E 合成データを生成する代わりに事前構築済みのデータセットを読み込む場合は、このガイドの末尾にある\u003Ca href=\"#appendix-a-load-pre-built-dataset-from-s3\"\u003E付録 A — S3 からの事前構築済みデータセットの読み込み\u003C/a\u003Eをご参照ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E4. データの探索 (EDA)\u003C/h2\u003E\n","\u003Cp\u003Eモデルをトレーニングする前に、顧客生涯価値を予測するための適切なフィーチャーを特定するためにパターンを分析します。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EDo exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E生成される内容\u003C/h3\u003E\n","\u003Cp\u003ECoCo はまずテーブルを確認してサマリー（行数・顧客数・日付範囲・カテゴリ内訳）を表示し、その後、購買頻度・支出分布・最新性パターン・カテゴリ嗜好などについて複数のステップにわたって詳細な分析を実施し、推奨フィーチャーとともに主要な所見をまとめます。\u003C/p\u003E\n","\u003Cp\u003Eテーブルが空（または存在しない）場合は、\u003Ca href=\"#appendix-a-load-pre-built-dataset-from-s3\"\u003E付録 A\u003C/a\u003E を参照して事前構築済みデータセットを読み込み、再試行してください。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-eda-feature-recommendations.jpg?v=23a691c1\" alt=\"EDA results and feature recommendations in CoCo\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eこの例では、CoCo は購買頻度トレンド・顧客セグメント別の平均注文額・最終購入からの経過日数・好みの商品カテゴリなどのシグナルを特定します。これらのインサイトは、total_transactions・avg_amount・days_since_last_purchase・favorite_category・channel_distribution などのフィーチャーに変換されます。\u003C/p\u003E\n","\u003Cp\u003EEDA ステップでは通常、以下のようなパターンが明らかになります。\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E高価値顧客はより頻繁に購入し、平均注文額が高い\u003C/li\u003E\u003Cli\u003E最終購入からの経過日数は将来の支出の強力な予測因子である\u003C/li\u003E\u003Cli\u003E特定の商品カテゴリは高い生涯価値と相関している\u003C/li\u003E\u003Cli\u003Eチャネル嗜好（web vs. モバイル vs. 店舗）は顧客セグメントによって異なる\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E5. モデルのトレーニング\u003C/h2\u003E\n","\u003Cp\u003Eフィーチャーが特定できたので、回帰モデルをトレーニングできます。XGBoost と Random Forest は、このような表形式の予測タスクに優れた選択肢です。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild those features and train a regression model to predict each customer's total spend in the next 90 days. Use two different algorithms, XGBoost and Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E生成される内容\u003C/h3\u003E\n","\u003Cp\u003ECoCo は通常、Notebook を作成し、フィーチャーエンジニアリングのステップを生成し、2 つのモデルをトレーニングして、最良のパフォーマンスを選択できるよう評価メトリクスをレポートします。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-train-two-models.jpg?v=23a691c1\" alt=\"Training and evaluation workflow created by CoCo\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eこの例では、CoCo はフィーチャーエンジニアリング（トランザクション履歴からの顧客ごとのメトリクスの集計）用の Python を生成し、トレーニング・評価ステップを実行し、最良のモデルを選択できるよう比較セクション（RMSE・MAE・R-squared などのメトリクス）を生成します。\u003C/p\u003E\n","\u003Cp\u003ECoCo は以下を実行します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEDA の推奨内容に基づいてフィーチャーをエンジニアリングする（トレーニングウィンドウでの顧客ごとの集計）\u003C/li\u003E\u003Cli\u003Eデータをトレーニング（80%）と評価（20%）のセットに分割する\u003C/li\u003E\u003Cli\u003E2 つの異なる回帰アルゴリズム（XGBoost と Random Forest）をトレーニングする\u003C/li\u003E\u003Cli\u003ERMSE・MAE・R-squared などのメトリクスを使用してパフォーマンスを比較する\u003C/li\u003E\u003Cli\u003Eパフォーマンスの優れたモデルを推奨する\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E次のステップに進む前に、評価メトリクスを確認してモデルが要件を満たしていることをご確認ください。\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E6. Model Registry へのログと推論の実行\u003C/h2\u003E\n","\u003Cp\u003Eより良いモデルを Snowflake Model Registry に登録し、バッチ推論を実行します。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ELog the better model with metrics into Snowflake Model Registry, and use Snowflake Warehouse for inference.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo は、モデルメトリクス・スキーマ推論用のサンプル入力・ターゲットプラットフォームを \u003Ccode\u003EWAREHOUSE\u003C/code\u003E に設定した適切なパラメータで \u003Ccode\u003Elog_model()\u003C/code\u003E の呼び出しを処理します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-model-registry-log-model.jpg?v=23a691c1\" alt=\"Model logged to Snowflake Model Registry with metrics\"\u003E\u003C/p\u003E\n","\u003Cp\u003E続いて予測を生成します。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate feature profiles for 50 customers and run LTV predictions for them. Show the top 10 highest predicted LTV customers.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo は顧客のフィーチャープロファイルを生成し、Snowflake Warehouse 経由で推論を実行し、各顧客の予測 90 日間支出（予測 LTV が高い順）を表示します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-batch-inference-100-requests.jpg?v=23a691c1\" alt=\"Batch inference results for LTV predictions\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eオプション:\u003C/strong\u003E 代わりにモデルを SPCS のリアルタイム REST エンドポイントとしてデプロイする場合は、このガイドの末尾にある付録 B — SPCS でのリアルタイム推論をご参照ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E7. エラーのデバッグと回復\u003C/h2\u003E\n","\u003Cp\u003E自然言語コーディングセッション中にエラーは避けられません。CoCo の優れた点は、状況・環境・エラーを評価して問題を自動的に修正する自己訂正能力です。\u003C/p\u003E\n","\u003Ch3\u003Eよくあるシナリオ\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eモデル登録エラー\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Elog_model()\u003C/code\u003E がパラメータの問題（ターゲットプラットフォームの不一致など）により失敗した場合、CoCo はエラーを診断し、修正されたパラメータで自動的にモデルを再登録します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENotebook 実行の問題\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003Eインポートの欠落やデータ型の不一致によってセルが失敗した場合、CoCo は問題を検出し、コードを調整して再実行します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eフィーチャーエンジニアリングのエラー\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003Eフィーチャーカラムが欠落しているか SQL ビューが失敗した場合、CoCo はスキーマを調査し、根本原因を特定して、フィーチャーエンジニアリングのステップを再生成します。\u003C/p\u003E\n","\u003Ch3\u003Eベストプラクティス\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E初期セットアップには \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E を使用し、その後専用ロールを作成する\u003C/li\u003E\u003Cli\u003ESPCS デプロイ中はコンピュートプールのリソースを監視する\u003C/li\u003E\u003Cli\u003ECoCo が修正を行う際の説明を確認する\u003C/li\u003E\u003Cli\u003Eビジュアライゼーションを含むインタラクティブな体験には Snowsight Notebook 環境を使用する\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E8. まとめとリソース\u003C/h2\u003E\n","\u003Cp\u003Eおめでとうございます！\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E での数回の自然言語プロンプトだけを使用して、顧客 LTV 予測モデルを完全に構築することができました。\u003C/p\u003E\n","\u003Ch3\u003E構築したもの\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/architecture-diagram.svg?v=23a691c1\" alt=\"LTV Prediction Pipeline Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E学習したこと\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E自然言語プロンプトを使用した現実的な合成 EC データの生成\u003C/li\u003E\u003Cli\u003E自動フィーチャー推奨による包括的な探索的データ分析の実施\u003C/li\u003E\u003Cli\u003ELTV 予測のための複数の回帰モデルのトレーニングと比較\u003C/li\u003E\u003Cli\u003ESnowflake Model Registry へのメトリクス付きモデルのログ\u003C/li\u003E\u003Cli\u003ESnowflake Warehouse でのバッチ推論の実行\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E関連リソース\u003C/h3\u003E\n","\u003Cp\u003EWeb ページ：\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E - Agentic ML を先導とする、開発・MLOps・推論のための統合機能セット\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/notebooks/\"\u003ESnowflake Notebooks\u003C/a\u003E - Snowflake Workspaces の Jupyter ベースのノートブック\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/snowflake-coco/\"\u003ECoCo\u003C/a\u003E - ML の生産性を向上させる Snowflake の AI ネイティブコーディングエージェント\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E技術ドキュメント：\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003ESnowflake ML Documentation\u003C/a\u003E - Snowflake ML 公式開発者ガイド\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart\"\u003ESnowflake ML Quickstart\u003C/a\u003E - Snowflake ML を始めるためのハンズオンガイド\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECoCo Documentation\u003C/a\u003E - CoCo 入門\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight\"\u003ECoCo in Snowsight\u003C/a\u003E - ブラウザベースの体験\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECoCo CLI\u003C/a\u003E - コマンドラインの体験\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E - ML モデルの登録・バージョン管理・デプロイ\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E - コンテナ化されたワークロードのデプロイと管理\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eオプション A - クリーンアップ\u003C/h2\u003E\n","\u003Cp\u003ESnowflake のクレジット消費を避けるために、このガイドで作成したリソースをクリーンアップできます。\u003Cstrong\u003ECoCo プロンプト\u003C/strong\u003Eと\u003Cstrong\u003E手動 SQL\u003C/strong\u003E の 2 つのアプローチがあります。\u003C/p\u003E\n","\u003Cp\u003Eこのクイックスタートを 1 回のセッションで完了し、ご利用の環境に他のデータが含まれていない場合は、「A-1 CoCo」プロンプトを使用してすばやくクリーンアップできます。\u003C/p\u003E\n","\u003Cp\u003Eこのクイックスタートを複数日にわたって実施した場合、またはご利用の環境にこのガイドとは無関係なリソースが含まれている場合は、意図したオブジェクトのみを削除するために「A-2 手動 SQL」アプローチを使用してください。\u003C/p\u003E\n","\u003Ch3\u003EA-1. CoCo\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: このプロンプトは、データベースやモデルなどのオブジェクトが作成された同じ CoCo セッション内で最も効果的です。前のセッション（例：別の日）のリソースをクリーンアップする場合、またはご利用の環境にこのクイックスタートとは無関係なオブジェクトが含まれている場合は、より正確な制御のために下記の手動 SQL アプローチを使用してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EDrop Database and model that we created earlier in this session\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo は各リソースに対応する DROP ステートメントを生成して実行します。\u003C/p\u003E\n","\u003Ch3\u003EA-2. 手動 SQL\u003C/h3\u003E\n","\u003Cp\u003E手動でクリーンアップを実行する場合：\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: \u003Ccode\u003ECOCO_DB.COCO_SCHEMA\u003C/code\u003E はデータベースとスキーマの例であり、\u003Ccode\u003ECOCO_WH\u003C/code\u003E はウェアハウス名の例です。ご利用の環境で CoCo が別のデータベースにデータを保存したか、別のウェアハウスを作成した場合は、クエリを実行する前にこれらの値を更新してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- データベースとその中のすべてのオブジェクト（テーブル、スキーマ、ステージなど）を削除する\nDROP DATABASE IF EXISTS COCO_DB;\n\n-- Model Registry からモデルを削除する\nDROP MODEL IF EXISTS COCO_DB.COCO_SCHEMA.ML_LTV_PREDICTOR;\n\n-- ウェアハウスを削除する\nDROP WAREHOUSE IF EXISTS COCO_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意: \u003Ccode\u003EDROP DATABASE\u003C/code\u003E はその中のすべてのスキーマ・テーブル・ステージを削除します。このコマンドを実行する前に、データが不要であることをご確認ください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eオプション B - CoCo CLI ウォークスルー\u003C/h2\u003E\n","\u003Cp\u003Eこのガイドで使用しているプロンプトはすべて、CoCo CLI でも同様に動作します。このセクションでは、CLI 固有のセットアップとターミナル出力のサンプルを示し、ターミナルセッションで期待される内容を比較できます。\u003C/p\u003E\n","\u003Ch3\u003Eセットアップ\u003C/h3\u003E\n","\u003Cp\u003ECLI をインストールします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eインストール後、\u003Ccode\u003Ecortex\u003C/code\u003E を実行してセットアップウィザードに従い、Snowflake アカウントに接続します。詳細な手順については、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECoCo CLI ドキュメント\u003C/a\u003Eをご参照ください。\u003C/p\u003E\n","\u003Cp\u003E接続を確認します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat role am I using and what databases can I access?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eヒント:\u003C/strong\u003E 生成されたファイルを並べて表示するには、VS Code または Cursor のターミナル内で CoCo CLI を実行してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eヒント:\u003C/strong\u003E テーブル名に \u003Ccode\u003E#\u003C/code\u003E プレフィックスを付けることで（例：\u003Ccode\u003E#COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS\u003C/code\u003E）、会話を特定のオブジェクトに紐付けることができます。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E合成データの生成 — CLI 出力\u003C/h3\u003E\n","\u003Cp\u003E合成データ生成のプロンプトを入力すると、CoCo CLI は次のようなサマリーを表示します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  Summary Statistics\n\n  ┌───────────┬──────────┐\n  │ Metric    │ Value    │\n  ├───────────┼──────────┤\n  │ Customers │ 500      │\n  ├───────────┼──────────┤\n  │ Min LTV   │ $12,930  │\n  ├───────────┼──────────┤\n  │ P25       │ $36,745  │\n  ├───────────┼──────────┤\n  │ Median    │ $54,617  │\n  ├───────────┼──────────┤\n  │ Mean      │ $76,769  │\n  ├───────────┼──────────┤\n  │ P75       │ $86,724  │\n  ├───────────┼──────────┤\n  │ Max       │ $495,857 │\n  ├───────────┼──────────┤\n  │ Std Dev   │ $67,972  │\n  └───────────┴──────────┘\n\n  Distribution (Histogram)\n\n    LTV Range           Customers\n    $12,930 - $36,868   126  █████████████████████████████████████\n    $37,086 - $61,126   170  ██████████████████████████████████████████████████\n    $61,382 - $84,439    74  ██████████████████████\n    $85,925 - $109,095   35  ██████████\n    $110,606 - $133,179  26  ████████\n    $134,850 - $157,594  18  █████\n    $158,039 - $181,760  19  ██████\n    $182,181 - $206,059   7  ██\n    $209,265 - $241,987  10  ███\n    $257,600 - $283,419   5  █\n    $345,815 - $387,939   7  ██\n    $410,296 - $495,857   3  █\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eデータの探索 (EDA) — CLI 出力\u003C/h3\u003E\n","\u003Cp\u003ECoCo CLI は組み込みの ML スキルを読み込み、ローカルの Python 環境を検出し、段階的に EDA を実行します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Do exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\n✓  SKILL  machine-learning\n\n✓  READ  cli-environment.md 194 lines\n\n✓  READ  ml-development/SKILL.md 279 lines\n\n* Let me set up the environment and build a comprehensive EDA script.\n\n✓  BASH  which uv\n  └─ /Users/shtanaka/.local/bin/uv\n\n✓  BASH  cortex env detect\n  └─ {"directory": "/Users/shtanaka/project/sfquickstarts", ...}\n\n✓  BASH  uv venv /Users/shtanaka/project/sfquickstarts/.venv --python 3.10 2>&1\n  └─ Creating virtual environment at: .venv\n\n✓  BASH  uv pip install snowflake-ml-python xgboost \n\n✓  BASH  python session_helper.py  (connectivity check)\n  └─ ✅ Connected successfully!\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/eda_ltv.py +330\n\n✓  BASH  python eda_ltv.py\n  └─ ... (99 more lines)\n\n✓  READ  eda_output/09_feature_importance_rf.png\n✓  READ  eda_output/04_correlation_with_target.png\n✓  READ  eda_output/01_target_distribution.png\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEDA 結果のサマリー：\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  EDA Results and Feature Recommendations\n\n  Dataset Overview\n  • 96,264 transactions, 500 customers, 18 months (2024-07-01 to 2025-12-30)\n  • Zero nulls across all 5 columns\n  • Feature/label split: 80,491 rows for features (before Oct 1, 2025),\n    15,773 rows for the 90-day target window\n\n  Key EDA Findings\n\n  Target variable (90-day spend):\n  • Right-skewed: median $8,767 vs mean $12,595\n  • Range: $1,265 to $85,492\n\n  Correlation analysis reveals two dominant signal families:\n  1. Monetary features (r = 0.73–0.93): TOTAL_SPEND, SPEND_90D, STDDEV_TXN_AMOUNT, AVG_TXN_AMOUNT\n  2. Frequency features (r = 0.58–0.72): TXN_COUNT, TXN_COUNT_90D, TXN_COUNT_30D\n\n  Random Forest baseline: 5-fold CV R² = 0.646\n\n  Recommended Features — Tier 1 (Strongest Predictors)\n\n  ┌───────────────────┬─────────────┬───────────────┐\n  │ Feature           │ Correlation │ RF Importance │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ TOTAL_SPEND       │ 0.93        │ 0.741         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_90D         │ 0.88        │ 0.041         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ STDDEV_TXN_AMOUNT │ 0.81        │ 0.054         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ AVG_TXN_AMOUNT    │ 0.79        │ 0.021         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_30D         │ 0.78        │ 0.004         │\n  └───────────────────┴─────────────┴───────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eモデルのトレーニング — CLI 出力\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Build those features and train a regression model to predict each customer's\n  total spend in the next 90 days. Use two different algorithms, XGBoost and\n  Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\n✓  READ  experiment-tracking/SKILL.md 456 lines\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/train_ltv_model.py +358\n\n* Script details:\n  • Features: 28 features from EDA (RFM, spend windows, category/channel shares, trends)\n  • Split: 80% train / 20% eval (random_state=42)\n  • Model 1 — XGBoost: 500 trees, lr=0.05, early stopping at 30 rounds\n  • Model 2 — Random Forest: 500 trees, max_depth=15\n  • Experiment tracking: Both runs logged to LTV_90D_SPEND_PREDICTION\n\n✓  BASH  python train_ltv_model.py\n  └─ ... training complete\n\n✓  READ  eda_output/10_actual_vs_predicted.png\n✓  READ  eda_output/11_residual_distributions.png\n✓  READ  eda_output/12_feature_importance_comparison.png\n\n  Model Comparison (20% Eval Set — 100 customers)\n\n  ┌────────┬─────────┬───────────────┬──────────────────────────┐\n  │ Metric │ XGBoost │ Random Forest │ Winner                   │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ R²     │ 0.8088  │ 0.7872        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ RMSE   │ $3,782  │ $3,989        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAE    │ $2,791  │ $2,787        │ Random Forest (marginal) │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAPE   │ 37.86%  │ 32.86%        │ Random Forest            │\n  └────────┴─────────┴───────────────┴──────────────────────────┘\n\n  Best Model: XGBoost (by R²)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E付録 A — S3 からの事前構築済みデータセットの読み込み\u003C/h2\u003E\n","\u003Cp\u003ECoCo で合成データを生成する代わりに事前構築済みのデータセットを読み込む場合は、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-from-a-sql-file\"\u003ESnowsight SQL ワークシート\u003C/a\u003Eで以下の SQL を実行するか、CoCo CLI にプロンプトとして貼り付けてください。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS COCO_DB;\nCREATE SCHEMA IF NOT EXISTS COCO_DB.COCO_SCHEMA;\nCREATE WAREHOUSE IF NOT EXISTS COCO_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE;\n\nUSE DATABASE COCO_DB;\nUSE SCHEMA COCO_SCHEMA;\nUSE WAREHOUSE COCO_WH;\n\nCREATE OR REPLACE FILE FORMAT ml_csvformat\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n  TYPE = 'CSV';\n\nCREATE OR REPLACE STAGE ml_ltv_data_stage\n  FILE_FORMAT = ml_csvformat\n  URL = 's3://sfquickstarts/sfguide_getting_started_with_cortex_code_for_ds_ml/ltv_transactions/';\n\nCREATE OR REPLACE TABLE ML_LTV_TRANSACTIONS (\n  CUSTOMER_ID VARCHAR(16777216),\n  TRANSACTION_TIME TIMESTAMP_NTZ(9),\n  AMOUNT NUMBER(12,2),\n  PRODUCT_CATEGORY VARCHAR(15),\n  CHANNEL VARCHAR(8)\n);\n\nCOPY INTO ML_LTV_TRANSACTIONS\n  FROM @ml_ltv_data_stage;\n\nSELECT 'Setup complete — ML_LTV_TRANSACTIONS loaded.' AS STATUS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESQL を実行した後、\u003Ca href=\"#explore-the-data-eda\"\u003Eデータの探索\u003C/a\u003Eステップに戻ってください。\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E付録 B — SPCS でのリアルタイム推論\u003C/h2\u003E\n","\u003Cp\u003ESnowflake Warehouse でのバッチ推論の代替として、Snowpark Container Services (SPCS) 上の REST エンドポイントとしてモデルをデプロイし、リアルタイム推論を行うことができます。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E前提条件:\u003C/strong\u003E Snowpark Container Services 用に設定されたコンピュートプール。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ELog the better model with metrics into Snowflake Model Registry, and use SPCS to create a REST endpoint for online inference.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo は \u003Ccode\u003Elog_model()\u003C/code\u003E の呼び出しをターゲットプラットフォームを \u003Ccode\u003ESNOWPARK_CONTAINER_SERVICES\u003C/code\u003E に設定して処理し、リアルタイム推論のための SPCS サービスとエンドポイントを作成します。\u003C/p\u003E\n","\u003Cp\u003E続いてレイテンシプロファイリングでエンドポイントをテストします。\u003C/p\u003E\n","\u003Ch3\u003Eプロンプト\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate feature profiles for 50 customers and run LTV predictions using the REST API for online inference running on SPCS. Show the top 10 highest predicted LTV customers and a latency profile (p50, p95, p99).\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo は SPCS REST エンドポイントに HTTP リクエストを送信し、以下を含む結果を表示します。\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E各顧客プロファイルの予測 90 日間支出\u003C/li\u003E\u003Cli\u003Eリクエストごとのレイテンシ測定値\u003C/li\u003E\u003Cli\u003Eデプロイメントのリアルタイムパフォーマンス特性を理解するためのレイテンシプロファイルのサマリー（p50・p95・p99）\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"SnowflakeのAgentic MLで始める機械学習モデル開発",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\u003E 🎥 **ガイド付きウォークスルーをご希望ですか？** [バーチャルハンズオンラボ](https://www.snowflake.com/en/webinars/virtual-hands-on-lab/build-your-first-agentic-ml-pipeline-with-natural-language-2026-05-28/) でご確認ください。\n\n## 1. 概要\n\n[Snowflake ML](http://www.snowflake.com/ml) は、Agentic ML の開発方法を変革しています。Agentic ML とは、自律的・推論ベースのシステムであり、開発者が ML パイプライン全体にわたるタスクの計画・実行にエージェントを活用できるようにするものです。このクイックスタートでは、Snowflake の AI ネイティブコーディングエージェントである [CoCo](https://www.snowflake.com/en/product/snowflake-coco/) （旧称Cortex Code）を使用し、数回のプロンプトだけで顧客生涯価値 (LTV) 予測モデルを構築・実行する方法を学びます。生データから本番予測まで、数週間ではなく数分で到達できます。CoCo は CLI として、また Snowflake の Web インターフェースである Snowsight から直接ご利用いただけます。\n\n\u003E **重要:** CoCo は LLM を基盤としており、非決定論的です。生成されるコードはこのガイドに示されているものと異なる場合があります。次のステップに進む前に、必ず出力を確認し、結果が期待通りであることをご確認ください。\n\n### 学習内容\n- 自然言語プロンプトを使用した現実的な合成 EC データの生成\n- 会話形式での探索的データ分析とフィーチャーエンジニアリングの実施\n- Snowflake 内での複数の回帰モデルのトレーニングと比較\n- Snowflake Model Registry へのメトリクス付きモデルのログ\n- Snowflake Warehouse でのバッチ推論の実行\n- （オプション）リアルタイム推論のための Snowpark Container Services (SPCS) への REST API としてのモデルデプロイ\n\n### 構築するもの\n顧客 LTV 予測の完全なパイプライン：\n- 合成 EC トランザクションデータセット（約 500 顧客、18 ヶ月間で約 100,000 件のトランザクション）\n- 顧客の今後 90 日間の総支出を予測する訓練済み回帰モデル\n- 評価メトリクス付きで Snowflake Model Registry に登録されたモデル\n- Snowflake Warehouse 経由のバッチ推論予測\n- （オプション）レイテンシプロファイリング付きの SPCS 上のリアルタイム推論 REST エンドポイント\n\n### 前提条件\n- Snowflake の 30 日間[無料トライアル](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)へのサインアップ。`ACCOUNTADMIN` ロール、またはデータベース・スキーマ・テーブル・モデルの作成権限を持つロール\n- [CoCo in Snowsight](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight)（ローカルインストール不要）\n- 専用の Snowflake Warehouse\n- （SPCS 利用時、オプション）Snowpark Container Services 用に設定されたコンピュートプール — [公式セットアップガイド](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup)をご参照ください\n- 基本的な ML の概念（トレーニング、評価、推論）に関する知識\n\n\u003E **CoCo CLI をご利用ですか？** 同じプロンプトはどちらのインターフェースでも動作します。CLI 固有のセットアップとターミナル出力の例については、[CoCo CLI ウォークスルー](#cortex-code-cli-walkthrough)をご参照ください。\n\n\u003C!-- ------------------------ --\u003E\n## 2. セットアップ\n\n### CoCo in Snowsight\n\n[CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) は Snowflake に組み込まれた AI エージェントであり、データエンジニアリング・アナリティクス・ML・エージェント構築タスク向けに設計されています。Snowflake 環境内で自律的に動作し、RBAC・スキーマ・プラットフォームのベストプラクティスに関する深い知識を活用します。\n\n1. サイドバーから「Projects」\u003E「Workspaces」をクリックして Workspace Notebook を開き、「My Workspace」パネルで「+ Add new」\u003E「Notebook」をクリックします。\n\n2. ノートブックが読み込まれたら、Snowsight の右下隅にある CoCo を確認します。\n\n\u003E 注意: CoCo は環境を認識しており、Workspace Notebook で使用すると、ノートブックが提供するすべてのツールにアクセスできるため、最良の結果が得られます。関連する場合、生成されたコードはノートブックに挿入され、自動的に実行されます。\n\n\nこれで、CoCo にプロンプトを入力して ML パイプラインの構築を開始する準備が整いました。\n\n\u003C!-- ------------------------ --\u003E\n## 3. 合成データの生成\n\nまず、CoCo を使用してデータベースオブジェクトを作成し、合成 EC トランザクションデータを生成します。\n\n### プロンプト\n\n```\nGenerate realistic looking synthetic data in database COCO_DB and schema COCO_SCHEMA \n(create if it doesn't exist). Create a table ML_LTV_TRANSACTIONS\nwith ~100000 transactions from ~500 customers over an 18-month period. Include\nCUSTOMER_ID, TRANSACTION_TIME, AMOUNT, PRODUCT_CATEGORY, and CHANNEL. Make the\ndata realistic: customers should have varying purchase frequencies (some buy\nweekly, others monthly), amounts should vary by category (electronics $50-$2000,\ngroceries $10-$200, apparel $20-$500), and channels should be web, mobile, or\nin-store. About 10% of customers should be high-value (frequent buyers with\nhigher average spend).\n```\n\n### 生成される内容\n\nCoCo のチャットパネルにプロンプトを入力します。CoCo はリクエストを分析し、複数ステップのプランに分解します。\n\n![Generate synthetic data by CoCo in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/generate-synthetic-data-by-cortex-code.jpg?v=23a691c1)\n\nCoCo はデータベースオブジェクトの作成とテーブルへのデータ投入のための SQL または Python コードを生成し、自動的に実行します。新しい Notebook セルにコードと結果が表示されます。\n\n![Basic statistics by CoCo in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/show-basic-statistics-by-cortex-code.jpg?v=23a691c1)\n\n\u003E 注意: LLM によるテキスト生成の固有のランダム性により、結果はこのチュートリアルに示されているものと若干異なる場合があります。\n\nデータが正しく生成されたことを確認するには、Snowsight ワークシートで以下の SQL を実行してください。\n\n\u003E 注意: `COCO_DB.COCO_SCHEMA` はデータベースとスキーマの例です。ご利用の環境で CoCo が別のデータベースまたはスキーマにデータを保存した場合は、クエリを実行する前にこれらの値を更新してください。\n\n```sql\nSELECT * FROM COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS LIMIT 10;\n```\n\n`CUSTOMER_ID`、`TRANSACTION_TIME`、`AMOUNT`、`PRODUCT_CATEGORY`、`CHANNEL` などのカラムを持つ 10 行が表示されるはずです。\n\n\u003E **代替手段:** 合成データを生成する代わりに事前構築済みのデータセットを読み込む場合は、このガイドの末尾にある[付録 A — S3 からの事前構築済みデータセットの読み込み](#appendix-a-load-pre-built-dataset-from-s3)をご参照ください。\n\n\u003C!-- ------------------------ --\u003E\n## 4. データの探索 (EDA)\n\nモデルをトレーニングする前に、顧客生涯価値を予測するための適切なフィーチャーを特定するためにパターンを分析します。\n\n### プロンプト\n\n```\nDo exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n```\n\n### 生成される内容\n\nCoCo はまずテーブルを確認してサマリー（行数・顧客数・日付範囲・カテゴリ内訳）を表示し、その後、購買頻度・支出分布・最新性パターン・カテゴリ嗜好などについて複数のステップにわたって詳細な分析を実施し、推奨フィーチャーとともに主要な所見をまとめます。\n\nテーブルが空（または存在しない）場合は、[付録 A](#appendix-a-load-pre-built-dataset-from-s3) を参照して事前構築済みデータセットを読み込み、再試行してください。\n\n![EDA results and feature recommendations in CoCo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-eda-feature-recommendations.jpg?v=23a691c1)\n\nこの例では、CoCo は購買頻度トレンド・顧客セグメント別の平均注文額・最終購入からの経過日数・好みの商品カテゴリなどのシグナルを特定します。これらのインサイトは、total_transactions・avg_amount・days_since_last_purchase・favorite_category・channel_distribution などのフィーチャーに変換されます。\n\nEDA ステップでは通常、以下のようなパターンが明らかになります。\n- 高価値顧客はより頻繁に購入し、平均注文額が高い\n- 最終購入からの経過日数は将来の支出の強力な予測因子である\n- 特定の商品カテゴリは高い生涯価値と相関している\n- チャネル嗜好（web vs. モバイル vs. 店舗）は顧客セグメントによって異なる\n\n\u003C!-- ------------------------ --\u003E\n## 5. モデルのトレーニング\n\nフィーチャーが特定できたので、回帰モデルをトレーニングできます。XGBoost と Random Forest は、このような表形式の予測タスクに優れた選択肢です。\n\n### プロンプト\n\n```\nBuild those features and train a regression model to predict each customer's total spend in the next 90 days. Use two different algorithms, XGBoost and Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n```\n\n### 生成される内容\n\nCoCo は通常、Notebook を作成し、フィーチャーエンジニアリングのステップを生成し、2 つのモデルをトレーニングして、最良のパフォーマンスを選択できるよう評価メトリクスをレポートします。\n\n![Training and evaluation workflow created by CoCo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-train-two-models.jpg?v=23a691c1)\n\nこの例では、CoCo はフィーチャーエンジニアリング（トランザクション履歴からの顧客ごとのメトリクスの集計）用の Python を生成し、トレーニング・評価ステップを実行し、最良のモデルを選択できるよう比較セクション（RMSE・MAE・R-squared などのメトリクス）を生成します。\n\nCoCo は以下を実行します。\n1. EDA の推奨内容に基づいてフィーチャーをエンジニアリングする（トレーニングウィンドウでの顧客ごとの集計）\n2. データをトレーニング（80%）と評価（20%）のセットに分割する\n3. 2 つの異なる回帰アルゴリズム（XGBoost と Random Forest）をトレーニングする\n4. RMSE・MAE・R-squared などのメトリクスを使用してパフォーマンスを比較する\n5. パフォーマンスの優れたモデルを推奨する\n\n次のステップに進む前に、評価メトリクスを確認してモデルが要件を満たしていることをご確認ください。\n\n\u003C!-- ------------------------ --\u003E\n## 6. Model Registry へのログと推論の実行\n\nより良いモデルを Snowflake Model Registry に登録し、バッチ推論を実行します。\n\n### プロンプト\n\n```\nLog the better model with metrics into Snowflake Model Registry, and use Snowflake Warehouse for inference.\n```\n\nCoCo は、モデルメトリクス・スキーマ推論用のサンプル入力・ターゲットプラットフォームを `WAREHOUSE` に設定した適切なパラメータで `log_model()` の呼び出しを処理します。\n\n![Model logged to Snowflake Model Registry with metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-model-registry-log-model.jpg?v=23a691c1)\n\n続いて予測を生成します。\n\n### プロンプト\n\n```\nCreate feature profiles for 50 customers and run LTV predictions for them. Show the top 10 highest predicted LTV customers.\n```\n\nCoCo は顧客のフィーチャープロファイルを生成し、Snowflake Warehouse 経由で推論を実行し、各顧客の予測 90 日間支出（予測 LTV が高い順）を表示します。\n\n![Batch inference results for LTV predictions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/snowsight-cortex-code-batch-inference-100-requests.jpg?v=23a691c1)\n\n\u003E **オプション:** 代わりにモデルを SPCS のリアルタイム REST エンドポイントとしてデプロイする場合は、このガイドの末尾にある付録 B — SPCS でのリアルタイム推論をご参照ください。\n\n\u003C!-- ------------------------ --\u003E\n## 7. エラーのデバッグと回復\n\n自然言語コーディングセッション中にエラーは避けられません。CoCo の優れた点は、状況・環境・エラーを評価して問題を自動的に修正する自己訂正能力です。\n\n### よくあるシナリオ\n\n**モデル登録エラー**\n\n`log_model()` がパラメータの問題（ターゲットプラットフォームの不一致など）により失敗した場合、CoCo はエラーを診断し、修正されたパラメータで自動的にモデルを再登録します。\n\n**Notebook 実行の問題**\n\nインポートの欠落やデータ型の不一致によってセルが失敗した場合、CoCo は問題を検出し、コードを調整して再実行します。\n\n**フィーチャーエンジニアリングのエラー**\n\nフィーチャーカラムが欠落しているか SQL ビューが失敗した場合、CoCo はスキーマを調査し、根本原因を特定して、フィーチャーエンジニアリングのステップを再生成します。\n\n### ベストプラクティス\n\n1. 初期セットアップには `ACCOUNTADMIN` を使用し、その後専用ロールを作成する\n2. SPCS デプロイ中はコンピュートプールのリソースを監視する\n3. CoCo が修正を行う際の説明を確認する\n4. ビジュアライゼーションを含むインタラクティブな体験には Snowsight Notebook 環境を使用する\n\n\n\u003C!-- ------------------------ --\u003E\n## 8. まとめとリソース\n\nおめでとうございます！[Snowflake ML](http://www.snowflake.com/ml) での数回の自然言語プロンプトだけを使用して、顧客 LTV 予測モデルを完全に構築することができました。\n\n### 構築したもの\n\n![LTV Prediction Pipeline Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-your-first-ml-model-in-snowflake-with-agentic-ml-ja/architecture-diagram.svg?v=23a691c1)\n\n### 学習したこと\n- 自然言語プロンプトを使用した現実的な合成 EC データの生成\n- 自動フィーチャー推奨による包括的な探索的データ分析の実施\n- LTV 予測のための複数の回帰モデルのトレーニングと比較\n- Snowflake Model Registry へのメトリクス付きモデルのログ\n- Snowflake Warehouse でのバッチ推論の実行\n\n### 関連リソース\n\nWeb ページ：\n- [Snowflake ML](http://www.snowflake.com/ml) - Agentic ML を先導とする、開発・MLOps・推論のための統合機能セット\n- [Snowflake Notebooks](https://www.snowflake.com/en/product/features/notebooks/) - Snowflake Workspaces の Jupyter ベースのノートブック\n- [CoCo](https://www.snowflake.com/en/product/snowflake-coco/) - ML の生産性を向上させる Snowflake の AI ネイティブコーディングエージェント\n\n技術ドキュメント：\n- [Snowflake ML Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview) - Snowflake ML 公式開発者ガイド\n- [Snowflake ML Quickstart](https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart) - Snowflake ML を始めるためのハンズオンガイド\n- [CoCo Documentation](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) - CoCo 入門\n- [CoCo in Snowsight](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight) - ブラウザベースの体験\n- [CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) - コマンドラインの体験\n- [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview) - ML モデルの登録・バージョン管理・デプロイ\n- [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview) - コンテナ化されたワークロードのデプロイと管理\n\n\u003C!-- ------------------------ --\u003E\n## オプション A - クリーンアップ\n\nSnowflake のクレジット消費を避けるために、このガイドで作成したリソースをクリーンアップできます。**CoCo プロンプト**と**手動 SQL** の 2 つのアプローチがあります。\n\nこのクイックスタートを 1 回のセッションで完了し、ご利用の環境に他のデータが含まれていない場合は、「A-1 CoCo」プロンプトを使用してすばやくクリーンアップできます。\n\nこのクイックスタートを複数日にわたって実施した場合、またはご利用の環境にこのガイドとは無関係なリソースが含まれている場合は、意図したオブジェクトのみを削除するために「A-2 手動 SQL」アプローチを使用してください。\n\n### A-1. CoCo\n\n\u003E 注意: このプロンプトは、データベースやモデルなどのオブジェクトが作成された同じ CoCo セッション内で最も効果的です。前のセッション（例：別の日）のリソースをクリーンアップする場合、またはご利用の環境にこのクイックスタートとは無関係なオブジェクトが含まれている場合は、より正確な制御のために下記の手動 SQL アプローチを使用してください。\n\n\n```\nDrop Database and model that we created earlier in this session\n```\n\nCoCo は各リソースに対応する DROP ステートメントを生成して実行します。\n\n### A-2. 手動 SQL\n\n手動でクリーンアップを実行する場合：\n\n\u003E 注意: `COCO_DB.COCO_SCHEMA` はデータベースとスキーマの例であり、`COCO_WH` はウェアハウス名の例です。ご利用の環境で CoCo が別のデータベースにデータを保存したか、別のウェアハウスを作成した場合は、クエリを実行する前にこれらの値を更新してください。\n\n\n```sql\n-- データベースとその中のすべてのオブジェクト（テーブル、スキーマ、ステージなど）を削除する\nDROP DATABASE IF EXISTS COCO_DB;\n\n-- Model Registry からモデルを削除する\nDROP MODEL IF EXISTS COCO_DB.COCO_SCHEMA.ML_LTV_PREDICTOR;\n\n-- ウェアハウスを削除する\nDROP WAREHOUSE IF EXISTS COCO_WH;\n```\n\n\u003E 注意: `DROP DATABASE` はその中のすべてのスキーマ・テーブル・ステージを削除します。このコマンドを実行する前に、データが不要であることをご確認ください。\n\n\n\u003C!-- ------------------------ --\u003E\n## オプション B - CoCo CLI ウォークスルー\n\nこのガイドで使用しているプロンプトはすべて、CoCo CLI でも同様に動作します。このセクションでは、CLI 固有のセットアップとターミナル出力のサンプルを示し、ターミナルセッションで期待される内容を比較できます。\n\n### セットアップ\n\nCLI をインストールします。\n\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n```\n\nインストール後、`cortex` を実行してセットアップウィザードに従い、Snowflake アカウントに接続します。詳細な手順については、[CoCo CLI ドキュメント](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli)をご参照ください。\n\n接続を確認します。\n\n```\nWhat role am I using and what databases can I access?\n```\n\n\u003E **ヒント:** 生成されたファイルを並べて表示するには、VS Code または Cursor のターミナル内で CoCo CLI を実行してください。\n\n\u003E **ヒント:** テーブル名に `#` プレフィックスを付けることで（例：`#COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS`）、会話を特定のオブジェクトに紐付けることができます。\n\n### 合成データの生成 — CLI 出力\n\n合成データ生成のプロンプトを入力すると、CoCo CLI は次のようなサマリーを表示します。\n\n```\n  Summary Statistics\n\n  ┌───────────┬──────────┐\n  │ Metric    │ Value    │\n  ├───────────┼──────────┤\n  │ Customers │ 500      │\n  ├───────────┼──────────┤\n  │ Min LTV   │ $12,930  │\n  ├───────────┼──────────┤\n  │ P25       │ $36,745  │\n  ├───────────┼──────────┤\n  │ Median    │ $54,617  │\n  ├───────────┼──────────┤\n  │ Mean      │ $76,769  │\n  ├───────────┼──────────┤\n  │ P75       │ $86,724  │\n  ├───────────┼──────────┤\n  │ Max       │ $495,857 │\n  ├───────────┼──────────┤\n  │ Std Dev   │ $67,972  │\n  └───────────┴──────────┘\n\n  Distribution (Histogram)\n\n    LTV Range           Customers\n    $12,930 - $36,868   126  █████████████████████████████████████\n    $37,086 - $61,126   170  ██████████████████████████████████████████████████\n    $61,382 - $84,439    74  ██████████████████████\n    $85,925 - $109,095   35  ██████████\n    $110,606 - $133,179  26  ████████\n    $134,850 - $157,594  18  █████\n    $158,039 - $181,760  19  ██████\n    $182,181 - $206,059   7  ██\n    $209,265 - $241,987  10  ███\n    $257,600 - $283,419   5  █\n    $345,815 - $387,939   7  ██\n    $410,296 - $495,857   3  █\n```\n\n### データの探索 (EDA) — CLI 出力\n\nCoCo CLI は組み込みの ML スキルを読み込み、ローカルの Python 環境を検出し、段階的に EDA を実行します。\n\n```\n\u003E Do exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\n✓  SKILL  machine-learning\n\n✓  READ  cli-environment.md 194 lines\n\n✓  READ  ml-development/SKILL.md 279 lines\n\n* Let me set up the environment and build a comprehensive EDA script.\n\n✓  BASH  which uv\n  └─ /Users/shtanaka/.local/bin/uv\n\n✓  BASH  cortex env detect\n  └─ {\"directory\": \"/Users/shtanaka/project/sfquickstarts\", ...}\n\n✓  BASH  uv venv /Users/shtanaka/project/sfquickstarts/.venv --python 3.10 2\u003E&1\n  └─ Creating virtual environment at: .venv\n\n✓  BASH  uv pip install snowflake-ml-python xgboost \n\n✓  BASH  python session_helper.py  (connectivity check)\n  └─ ✅ Connected successfully!\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/eda_ltv.py +330\n\n✓  BASH  python eda_ltv.py\n  └─ ... (99 more lines)\n\n✓  READ  eda_output/09_feature_importance_rf.png\n✓  READ  eda_output/04_correlation_with_target.png\n✓  READ  eda_output/01_target_distribution.png\n```\n\nEDA 結果のサマリー：\n\n```\n  EDA Results and Feature Recommendations\n\n  Dataset Overview\n  • 96,264 transactions, 500 customers, 18 months (2024-07-01 to 2025-12-30)\n  • Zero nulls across all 5 columns\n  • Feature/label split: 80,491 rows for features (before Oct 1, 2025),\n    15,773 rows for the 90-day target window\n\n  Key EDA Findings\n\n  Target variable (90-day spend):\n  • Right-skewed: median $8,767 vs mean $12,595\n  • Range: $1,265 to $85,492\n\n  Correlation analysis reveals two dominant signal families:\n  1. Monetary features (r = 0.73–0.93): TOTAL_SPEND, SPEND_90D, STDDEV_TXN_AMOUNT, AVG_TXN_AMOUNT\n  2. Frequency features (r = 0.58–0.72): TXN_COUNT, TXN_COUNT_90D, TXN_COUNT_30D\n\n  Random Forest baseline: 5-fold CV R² = 0.646\n\n  Recommended Features — Tier 1 (Strongest Predictors)\n\n  ┌───────────────────┬─────────────┬───────────────┐\n  │ Feature           │ Correlation │ RF Importance │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ TOTAL_SPEND       │ 0.93        │ 0.741         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_90D         │ 0.88        │ 0.041         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ STDDEV_TXN_AMOUNT │ 0.81        │ 0.054         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ AVG_TXN_AMOUNT    │ 0.79        │ 0.021         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_30D         │ 0.78        │ 0.004         │\n  └───────────────────┴─────────────┴───────────────┘\n```\n\n### モデルのトレーニング — CLI 出力\n\n```\n\u003E Build those features and train a regression model to predict each customer's\n  total spend in the next 90 days. Use two different algorithms, XGBoost and\n  Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\n✓  READ  experiment-tracking/SKILL.md 456 lines\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/train_ltv_model.py +358\n\n* Script details:\n  • Features: 28 features from EDA (RFM, spend windows, category/channel shares, trends)\n  • Split: 80% train / 20% eval (random_state=42)\n  • Model 1 — XGBoost: 500 trees, lr=0.05, early stopping at 30 rounds\n  • Model 2 — Random Forest: 500 trees, max_depth=15\n  • Experiment tracking: Both runs logged to LTV_90D_SPEND_PREDICTION\n\n✓  BASH  python train_ltv_model.py\n  └─ ... training complete\n\n✓  READ  eda_output/10_actual_vs_predicted.png\n✓  READ  eda_output/11_residual_distributions.png\n✓  READ  eda_output/12_feature_importance_comparison.png\n\n  Model Comparison (20% Eval Set — 100 customers)\n\n  ┌────────┬─────────┬───────────────┬──────────────────────────┐\n  │ Metric │ XGBoost │ Random Forest │ Winner                   │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ R²     │ 0.8088  │ 0.7872        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ RMSE   │ $3,782  │ $3,989        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAE    │ $2,791  │ $2,787        │ Random Forest (marginal) │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAPE   │ 37.86%  │ 32.86%        │ Random Forest            │\n  └────────┴─────────┴───────────────┴──────────────────────────┘\n\n  Best Model: XGBoost (by R²)\n```\n\n\u003C!-- ------------------------ --\u003E\n## 付録 A — S3 からの事前構築済みデータセットの読み込み\n\nCoCo で合成データを生成する代わりに事前構築済みのデータセットを読み込む場合は、[Snowsight SQL ワークシート](https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-from-a-sql-file)で以下の SQL を実行するか、CoCo CLI にプロンプトとして貼り付けてください。\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS COCO_DB;\nCREATE SCHEMA IF NOT EXISTS COCO_DB.COCO_SCHEMA;\nCREATE WAREHOUSE IF NOT EXISTS COCO_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE;\n\nUSE DATABASE COCO_DB;\nUSE SCHEMA COCO_SCHEMA;\nUSE WAREHOUSE COCO_WH;\n\nCREATE OR REPLACE FILE FORMAT ml_csvformat\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n  TYPE = 'CSV';\n\nCREATE OR REPLACE STAGE ml_ltv_data_stage\n  FILE_FORMAT = ml_csvformat\n  URL = 's3://sfquickstarts/sfguide_getting_started_with_cortex_code_for_ds_ml/ltv_transactions/';\n\nCREATE OR REPLACE TABLE ML_LTV_TRANSACTIONS (\n  CUSTOMER_ID VARCHAR(16777216),\n  TRANSACTION_TIME TIMESTAMP_NTZ(9),\n  AMOUNT NUMBER(12,2),\n  PRODUCT_CATEGORY VARCHAR(15),\n  CHANNEL VARCHAR(8)\n);\n\nCOPY INTO ML_LTV_TRANSACTIONS\n  FROM @ml_ltv_data_stage;\n\nSELECT 'Setup complete — ML_LTV_TRANSACTIONS loaded.' AS STATUS;\n```\n\nSQL を実行した後、[データの探索](#explore-the-data-eda)ステップに戻ってください。\n\n\u003C!-- ------------------------ --\u003E\n## 付録 B — SPCS でのリアルタイム推論\n\nSnowflake Warehouse でのバッチ推論の代替として、Snowpark Container Services (SPCS) 上の REST エンドポイントとしてモデルをデプロイし、リアルタイム推論を行うことができます。\n\n\u003E **前提条件:** Snowpark Container Services 用に設定されたコンピュートプール。\n\n### プロンプト\n\n```\nLog the better model with metrics into Snowflake Model Registry, and use SPCS to create a REST endpoint for online inference.\n```\n\nCoCo は `log_model()` の呼び出しをターゲットプラットフォームを `SNOWPARK_CONTAINER_SERVICES` に設定して処理し、リアルタイム推論のための SPCS サービスとエンドポイントを作成します。\n\n続いてレイテンシプロファイリングでエンドポイントをテストします。\n\n### プロンプト\n\n```\nCreate feature profiles for 50 customers and run LTV predictions using the REST API for online inference running on SPCS. Show the top 10 highest predicted LTV customers and a latency profile (p50, p95, p99).\n```\n\nCoCo は SPCS REST エンドポイントに HTTP リクエストを送信し、以下を含む結果を表示します。\n- 各顧客プロファイルの予測 90 日間支出\n- リクエストごとのレイテンシ測定値\n- デプロイメントのリアルタイムパフォーマンス特性を理解するためのレイテンシプロファイルのサマリー（p50・p95・p99）\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c321664c23","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-06fab1c424",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-47a05588ff","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-07-13",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-70dd4fa06a","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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