AI leader DataRobot announced the availability of DataRobot Notebooks, a fully integrated notebooks solution within the DataRobot AI platform that enables data scientists to collaborate across code-first workflows with one-click access to embedded notebooks.
Notebooks are a crucial tool for data scientists to rapidly experiment and share insights through quick environment creation, interactive computation, and code snippets. As the number of notebook users in a data science organization grows, challenges including managing notebooks at scale and maintaining complex dependencies and libraries become overwhelming and costly for data science teams.
“We are entering a phase of AI governance where the collaboration and productivity gains of data science teams become increasingly important,” said Mike Leone, Senior Analyst at Enterprise Strategy Group. “With DataRobot Notebooks, the flexibility to develop in preferred environments, including open-source ML tooling or in the DataRobot AI platform, streamlines the code development experience and allows data scientists to better collaborate as a team in a unified environment.”
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DataRobot Notebooks streamlines the code development experience for data science workflows, with an emphasis on automation, reproducibility, scalability, and collaboration. This enhanced capability brings unique value to data science teams with:
- Interoperability: DataRobot Notebooks is compatible and interoperable with the Jupyter Notebook standard, accelerating onboarding onto the DataRobot AI platform. DataRobot Notebooks come with pre-defined, pre-installed containerized environments that have frequently-used open-source machine learning libraries, including NumPy, Seaborn, scikit-learn, SciPy, and more.
- Native integration within DataRobot: DataRobot Notebooks is fully integrated with the DataRobot ecosystem, allowing data scientists to run their code directly on the platform with all the libraries and tools they need. With this deep integration, DataRobot Notebooks serves as a code-centric solution for users leveraging DataRobot AutoML and MLOps capabilities.
- Centralized management: DataRobot Notebooks is a unified environment with centralized governance and fine-grained access controls, so, data scientists can easily organize, collaborate, and share notebooks and related assets among individuals and teams.
- Enhanced features: Users can now write and execute custom code in cloud-based notebooks with access to private, scalable, and containerized computing environments. DataRobot Notebooks also provides version history, code snippets, code intelligence capabilities like code completion, credentials management, built-in visualizations, and more.
“Customers want a notebook solution that will allow them to focus on their data science work rather than infrastructure management,” said Venky Veeraraghavan, SVP of Product at DataRobot. “With DataRobot Notebooks, data science teams can leverage a fully-managed, secure, and cloud-first solution that helps make their work a true team sport. By providing the foundation for success and removing infrastructure maintenance, DataRobot Notebooks users can easily make progress and collaborate as a team.”
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