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This section provides preliminary documentation for features currently in the MLOps public preview pipeline. They have been tested within the engineering and quality environments and are available for preview by a limited number of users. If not enabled for your organization, the feature is not visible. Note that functionality or documentation specifics are subject to change.




パブリックプレビュー 説明...
デプロイ用のTableauの分析拡張機能 Tableauの分析拡張機能を使用して、DataRobotの予測をTableauプロジェクトに統合します。
バッチ予測APIのマルチパートアップロード スコアリングデータを複数のファイルにアップロードして、大規模なデータセットのファイル取り込みを改善します。
カスタムモデルやタスクのためのリモートリポジトリファイルブラウザー Browse the folders and files in a remote repository to select the files you want to add to a custom model or task.
カスタム指標のためのデプロイ予測データとトレーニングデータのエクスポート Export a deployment's stored prediction and training data to compute and monitor custom business or performance metrics outside DataRobot.
Deployment Usage tab Tracks prediction processing progress for use in accuracy, data drift, and predictions over time analysis.
[[データドリフト]タブでのドリルダウン](pp-drift-drill-down) Visualize changes in the drift status over time as a heat map for each tracked feature and monitor the difference in data distribution between time periods for selected features.
Expanded batch prediction job definition access Extend role-based access controls for deployments to the associated batch prediction job definitions.
Model logs for model packages View model logs for model packages from the Model Registry to see successful operations (INFO status) and errors (ERROR status).
Model package artifact creation workflow The improved model package artifact creation workflow provides a clearer and more consistent path to model deployment, with visible connections between a model and its associated model packages.
GitHub Actions for custom models The custom models action manages custom inference models and deployments in DataRobot via GitHub CI/CD workflows.

更新しました January 11, 2023
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