カスタムモデルワークショップ¶
本機能の提供について
カスタムモデルワークショップは、DataRobot MLOps専用の機能です。 この機能を有効にする方法については、DataRobotの担当者にお問い合わせください。
The Custom Model Workshop allows you to upload model artifacts to create, test, and deploy custom inference models to a centralized model management and deployment hub. Custom inference models are pre-trained, user-defined models that support most of DataRobot's MLOps features. DataRobot supports custom inference models built in a variety of languages, including Python, R, and Java. If you've created a model outside of DataRobot and you want to upload your model to DataRobot, you need to define the model content and the model environment in the Custom Model Workshop.
重要
Custom inference models are not custom DataRobot models. They are user-defined models created outside of DataRobot and assembled in the Custom Model Workshop for access to deployment, monitoring, and governance. To support the local development of the models you want to bring into DataRobot through the Custom Model Workshop, the DataRobot Model Runner (or DRUM) provides you with tools to locally assemble, debug, test, and run the inference model before assembly in DataRobot. Before adding a custom model to the workshop, DataRobot recommends you reference the custom model assembly guidelines for building a custom model to upload to the workshop.
The following topics describe how you can manage custom model artifacts in DataRobot:
| トピック | 説明 |
|---|---|
| Create custom models | How to create custom inference models in the Custom Model Workshop. |
| Manage custom model dependencies | How to manage model dependencies from the workshop and update the base drop-in environments to support your model code. |
| Manage custom model resource usage | How to configure the resources a model consumes to facilitate smooth deployment and minimize potential environment errors in production. |
| Add custom model versions | How to to create a new version of the model and/or environment after updating the file contents with new package versions, different preprocessing steps, updated hyperparameters, and more. |
| Add training data to a custom model | How to add training data to a custom inference model for deployment. |
| リモートリポジトリのファイルをカスタムモデルに追加 | リモートリポジトリに接続し、カスタムモデルのファイルをカスタムモデルワークショップにプルする方法。 |
| Test a custom model in DataRobot | カスタムモデルワークショップでカスタム推論モデルをテストする方法。 |
| カスタムモデルの管理 | カスタムモデルとカスタムモデル環境を削除または共有する方法。 |
| Register custom models as model packages | モデルレジストリにカスタム推論モデルを登録する方法。 |
| カスタムモデルパッケージの管理 | モデルレジストリからカスタムモデルをデプロイ、共有、またはアーカイブする方法。 |
DataRobotによって管理される予測サーバーにデプロイした後、API経由で予測を作成し、一連の機能を使用してデプロイを監視できます。