What are DataRobot Notebooks?

DataRobot Notebooks are a fully managed, hosted offering with scalable compute resources. This notebook solution is embedded within our enterprise AI platform and helps drive productivity, efficiency, and collaboration across data science teams. This unlocks additional flexibility by allowing code-first data scientists to write their own tasks and build custom models, extending the out-of-the-box capabilities provided by DataRobot. Notebooks provide a unified environment for your entire team to collaborate securely, allowing team members to natively use notebooks within DataRobot in just one click.

What are the benefits of DataRobot Notebooks over alternative open-source solutions (e.g. Jupyter, JupyterLab)?

Working locally with notebooks can be challenging to scale for an enterprise, but 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 rapidly experiment and collaborate as a team. Since DataRobot Notebooks are fully managed, you can work with notebooks using scalable resources without the tedious task of managing and configuring low-level infrastructure. DataRobot Notebooks also provides enhanced features beyond the classic Jupyter offering, such as version history, credentials management, built-in visualizations, and more. Code snippets for frequently used data science functions, as well as integrations with DataRobot APIs, help ensure that data science professionals spend less time looking up boilerplate code and more time on creative, rapid experimentation. Users will be able to have the full flexibility of a code-first environment while also leveraging the rich suite of other DataRobot ML offerings, including automated machine learning.

How do DataRobot Notebooks deliver business impact to organizations?

By enabling collaboration between code-first data scientists and GUI-centric business users, DataRobot Notebooks increases the efficiency of development across the machine learning lifecycle, from data exploration and data preparation to model deployment and continued optimization. DataRobot uses a centralized AI development infrastructure to help ensure visibility into your IP by storing notebooks and other ML assets in the cloud. Centralization simplifies governance, organization, and dependency management within notebooks, ensuring reproducibility by technical and non-technical users. Free up your team to drive business impact, rather than maintaining your underlying infrastructure.

Notebooks

Build and automate end-to-end solutions to drive data analysis, model development, and productionization across the machine learning lifecycle.

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Data Science Notebooks for Code-First Users

AI is a team sport that requires effective collaboration among data scientists, business analysts, IT, and other groups across the enterprise. Over 30% of data scientists collaborate on teams with 10 or more data science experts, each with their own preference of tools and languages. Organizations need a unified environment that provides the flexibility for code-first experimentation coupled with out-of-the-box enterprise features to execute at scale. Extend the power of the DataRobot AI platform to expert data scientists who love to code.

Integrated Experience that Drives Real Business Impact

Enable your team to build data science notebooks with flexibility and control through the power of the DataRobot AI platform. Reduce hundreds of lines of code, automate data science tasks, and accommodate custom code workflows specific to your business needs. Innovate and experiment fast to deliver unique insights from any data.

Enterprise-Grade ML Development at Scale

Accelerate AI delivery by leveraging the managed and scalable compute infrastructure along with enterprise-grade security on the DataRobot AI platform. Easily configure hardware and environment resources. Spend more time delivering impact for the business and less time on infrastructure management.

Centralized Governance and Compliance

Centralize AI development by storing your team’s data science notebooks in the cloud rather than on local machines, helping ensure visibility into your IP. Establish governance and policies by easily defining user roles and designated privileges to protect sensitive code, data, and platform management functions from unwanted access.

Streamline Data Science Workflows with Native Capabilities Beyond Classic Jupyter Notebooks

DataRobot Notebooks are Jupyter compatible, ensuring a familiar user interface and interoperability with the rest of the ecosystem. A host of native features further enhances the code-centric data science development experience. Code snippets for a variety of common data science and DataRobot API tasks, such as connecting to external data sources and building models with DataRobot Autopilot and generating custom metrics. Notebook environment variables allow you to securely store and manage secrets. Built–in notebook revision history provides the ability to checkpoint and track your progress and revert to previous versions of notebooks as needed.

Many customers have told us they want a secure, embedded, and cloud-first solution to break free from open-source notebooks and their high costs to maintain and lack of collaboration,” said Venky Veeraraghavan, SVP Product Management at DataRobot. “Now, with DataRobot Notebooks, anyone can quickly and easily migrate their open-source notebooks or legacy code to DataRobot, giving customers the best of both worlds—the native integration with the the DataRobot AI platform and a best in class notebook experience with consumption based pricing so you only pay for what you use.

Venky Veeraraghavan
Chief Product Officer

Frequently Asked Questions

Discover how to empower code-first data science experts through a unified experience that accelerates AI development.

What are DataRobot Notebooks?

DataRobot Notebooks are a fully managed, hosted offering with scalable compute resources. This notebook solution is embedded within our enterprise AI platform and helps drive productivity, efficiency, and collaboration across data science teams. This unlocks additional flexibility by allowing code-first data scientists to write their own tasks and build custom models, extending the out-of-the-box capabilities provided by DataRobot.

Notebooks provide a unified environment for your entire team to collaborate securely, allowing team members to natively use notebooks within DataRobot in just one click.
What are the benefits of DataRobot Notebooks over alternative open-source solutions (e.g. Jupyter, JupyterLab)?

Working locally with notebooks can be challenging to scale for an enterprise, but 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 rapidly experiment and collaborate as a team. Since DataRobot Notebooks are fully managed, you can work with notebooks using scalable resources without the tedious task of managing and configuring low-level infrastructure.

DataRobot Notebooks also provides enhanced features beyond the classic Jupyter offering, such as version history, credentials management, built-in visualizations, and more. Code snippets for frequently used data science functions, as well as integrations with DataRobot APIs, help ensure that data science professionals spend less time looking up boilerplate code and more time on creative, rapid experimentation.

Users will be able to have the full flexibility of a code-first environment while also leveraging the rich suite of other DataRobot ML offerings, including automated machine learning.
How do DataRobot Notebooks deliver business impact to organizations?

By enabling collaboration between code-first data scientists and GUI-centric business users, DataRobot Notebooks increases the efficiency of development across the machine learning lifecycle, from data exploration and data preparation to model deployment and continued optimization.

DataRobot uses a centralized AI development infrastructure to help ensure visibility into your IP by storing notebooks and other ML assets in the cloud. Centralization simplifies governance, organization, and dependency management within notebooks, ensuring reproducibility by technical and non-technical users. Free up your team to drive business impact, rather than maintaining your underlying infrastructure.