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.
|Use cases for version 2.x||Notebooks for uses cases that use methods for 2.x versions of DataRobot's Python client.|
|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.|
|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.|
|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.|
|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.|
|設備故障の予測||A use case that that determines whether or not equipment part failure will occur.|
|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|