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Notebook reference


Are DataRobot Notebooks available in Classic and Workbench?

Yes, DataRobot Notebooks are available in both Workbench and DataRobot Classic.

Why should I use DataRobot Notebooks?

DataRobot Notebooks offer you the flexibility to develop and run code in the language of your choice using both your preferred open-source ML libraries as well as the DataRobot API for streamlining and automating your DataRobot workflows—all within the DataRobot platform. With the fully managed, hosted platform for creating and executing notebooks with auto-scaling capabilities, you can focus on data science work rather than infrastructure management. You can easily organize, collaborate, and share notebooks and related assets among your teams in one unified environment with centralized governance via DataRobot Use Cases.

What’s different about DataRobot Notebooks compared to Jupyter?

Working locally with open-source Jupyter Notebooks can be challenging to scale with an enterprise, whereas DataRobot Notebooks help make data science a team sport by serving as a central repository for notebooks and data science assets, so that you can easily make progress and collaborate as a team. Since DataRobot Notebooks is a fully managed solution, you can work with notebooks with scalable resources without having to manage the infrastructure yourself. DataRobot Notebooks also provide enhanced features beyond the classic Jupyter offering, such as built-in revision history, credential management, built-in visualizations, and more. You will be able to have the full flexibility of a code-first environment while also leveraging DataRobot’s suite of other ML offerings, including automated machine learning.

Are DataRobot Notebooks compatible with Jupyter?

Yes, notebooks are Jupyter compatible and utilize Jupyter kernels. DataRobot supports import from and export to the .ipynb standard file format for notebook files, so you can easily bring existing workloads into the platform without concern for vendor lock-in of your IP. The user interface is also closely aligned with Jupyter (e.g. modal editor, keyboard shortcuts), so you can easily onboard without a steep learning curve.

Can I install any packages I need within the notebook environment?

Yes, you can install any additional packages you need into your environment at runtime during your notebook session. You can use Jupyter magic commands (e.g. !pip install <package-name>) from a notebook cell. Note that each time your session shuts down, those packages installed at runtime will not be persisted, and you will have to reinstall them next time you restart the session.

Can I share notebooks?

Although you cannot share notebooks directly with other users, in Workbench you can share Use Cases that contain notebooks. Therefore, to share a notebook with another user, you must share the entire Use Case so that they have access to all associated assets.

How can I access datasets in my Use Case that I have not yet loaded into my notebook?

Access the dataset you want to include in the notebook from the Use Case dashboard. The ID is included in the dataset URL (after /prepare/); it is the same ID stored for the dataset in the AI Catalog.


DataRobot offers a library of common use cases that outline data science and machine learning workflows to solve problems. You can view a selection of notebooks below that use v3.0 of DataRobot's Python client.

トピック 内容…
Use cases for version 2.x Notebooks for uses cases that use methods for 2.x versions of DataRobot's Python client.
異常検知によるマネーロンダリングの特定 過去の金銭取引データセットを使用して、マネーロンダリングのインスタンスを検出するモデルをトレーニングします。
需要の価格弾力性の測定 価格と需要の関係を特定し、製品の価格を適切に設定して収益を最大化し、価格と需要の変化に対する価格の弾力性を監視するためのユースケース。
Insurance claim triage How to evaluate the severity of an insurance claim in order to triage it effectively.
債務不履行の予測 A use case that reduces defaults and minimizes risk by predicting the likelihood that a borrower will not repay their loan.
ノーショー予約の予想 関連する理由とともに、予約をすっぽかす確率が最も高い患者を特定するモデルを構築します。
出荷遅れの予測 A use case that determines whether a shipment will be late or if there will be a shortage of parts.
30日間の再入院率の低下 How to reduce the 30-day readmission rate at a hospital.
鋼板の欠陥の予測 製造業者があらゆる種類の欠陥(鋼板の欠陥など)を特定する、効率と有効性を大幅に改善するのに役立つユースケース。
Predict customer churn 解約のリスクがある顧客を予測する方法と、それを防ぐために介入するタイミング
大規模な需要予測 An end-to-end demand forecasting use case that uses DataRobot's Python package.
夢の野球チームの予測 野球選手の本当の才能レベルおよび来シーズンの予想されるパフォーマンスの推定値。
リードスコアリング A binary classification problem of whether a prospect will become a customer.
複数系列モデリングによる売上予測 How to forecast future sales for multiple stores using multiseries modeling.
異常検知によるマネーロンダリングの特定 How to train anomaly detection models to detect outliers.
時間外検定モデリングによるCO2レベルの予測 DataRobotのPythonクライアントで 時間外検定(OTV)モデリングを使用する方法を学び、ハワイの活火山の1つであるマウナロアの毎月のCO₂レベルを予測します。
設備故障の予測 A use case that that determines whether or not equipment part failure will occur.
医療費の不正請求の予測 DataRobot Pythonパッケージを使用して、医療費の不正請求を特定します。
Generate SHAP-based Prediction Explanations DataRobotでSHAPによる予測の説明を使って、住宅のどのような特長が売却価値を高めるかを見極める方法

更新しました April 19, 2023
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