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, has been made available, according to AI leader DataRobot.
Data scientists use notebooks as a key tool to quickly explore and communicate insights through interactive computation, simple environment setup, and code snippets. The challenges of managing notebooks at scale and maintaining complex dependencies and libraries become onerous and expensive for data science teams as the number of notebook users in an organisation increases.
“We are entering a phase of AI governance where the collaboration and productivity gains of data science teams become increasingly important”, “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.”
Mike Leone, Senior Analyst at Enterprise Strategy Group
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:
The Jupyter Notebook standard is compatible and interoperable with DataRobot Notebooks, facilitating faster adoption of the DataRobot AI platform. The pre-configured, pre-installed containerized environments that come with DataRobot Notebooks include SciPy, NumPy, Seaborn, and other commonly used open-source machine learning libraries.
Native DataRobot integration:
Data scientists can run their code directly on the platform with all the libraries and tools they require thanks to DataRobot Notebooks’ complete integration with the DataRobot ecosystem. DataRobot Notebooks now provides users who are utilising the AutoML and MLOps features of DataRobot with a code-centric solution.
Data scientists can effortlessly organise, communicate, and share notebooks and related assets among individuals and teams thanks to the unified environment provided by DataRobot Notebooks, which has centralised governance and fine-grained access restrictions.
Users now have access to private, scalable, and containerized computing environments in cloud-based notebooks where they may write and run custom code. Additionally, DataRobot Notebooks has built-in visualizations, credentials management, code completion, code snippets, version history, and more.
“Customers want a notebook solution that will allow them to focus on their data science work rather than infrastructure management”, “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.”
Venky Veeraraghavan, SVP of Product at DataRobot