DataRobot provides drop-in environments in the Custom Model Workshop. Drop-in environments contain the web server Scoring Code and a
start_server.sh file required for a custom model so that you don't need to provide them in the model's folder. The following table details the drop-in environments provided by DataRobot. Each environment is prefaced with [DataRobot] in the Environments tab of the Custom Model Workshop. You can select these drop-in environments when you create a custom model.
|Environment name & example||Model compatibility & artifact file extension|
|Python 3 ONNX Drop-In||ONNX models (
|Python 3 PMML Drop-In||PMML models (
|Python 3 PyTorch Drop-In||PyTorch models (
|Python 3 Scikit-Learn Drop-In||Scikit-Learn models (
|Python 3 XGBoost Drop-In||Native XGBoost models (
|Python 3 Keras Drop-In||Keras models backed by tensorflow (
|Java Drop-In||DataRobot Scoring Code models (
|R Drop-in Environment||R models trained using CARET (
Due to the time required to install all libraries recommended by CARET, only model types that are also package names are installed (e.g.,
|Julia Drop-In*||Julia models (
* The Julia drop-in environment isn't officially supported; it is provided as an example.
All Python environments contain Scikit-Learn to help with preprocessing (if necessary), but only Scikit-Learn can make predictions on sklearn models.
Updated March 15, 2023
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