Posit Workbench provides centralized management of data science environments that are configured and ready to use. It allows data scientists to be self-sufficient, spinning up their own workspaces as needed, while IT maintains control and governance around the tools they use. Posit Workbench integrates with existing security and access systems to provide a streamlined experience for data scientists while ensuring data access is controlled and governed. It also provides centralized configuration, upgrades, and patching of JupyterLab, RStudio, and VS Code environments to reduce the burden on technology teams supporting data scientists. Real-time monitoring and unified observability via Prometheus metrics help you deliver reliability and trust. By providing a common set of tools for R and Python, Posit Workbench reduces onboarding times and facilitates collaboration by providing a standard set of Python and R packages or Docker images. It also allows you to create custom resource profiles to control access to CPU, memory, and GPUs for different projects or users, and handle teams of any size, up to thousands of users via Kubernetes or Slurm.

Features:

• Seamless integration with your security and access systems: Posit Workbench integrates with your existing systems to provide a streamlined experience for data scientists while ensuring that data access is controlled and governed. Workbench-managed Credentials for AWS, Azure, and Databricks provide a seamless and secure authorization layer across your data ecosystem.
• Open source data science for the enterprise: Posit Workbench provides centralized configuration, upgrades, and patching of JupyterLab, RStudio, and VS Code environments to reduce burden on technology teams supporting data scientists. Real-time monitoring and unified observability via Prometheus metrics help you deliver reliability and trust.
• Better collaboration with standardized environments: Reduce onboarding times by providing a common set of tools for R and Python. Facilitate collaboration by providing a standard set of Python and R packages or Docker images. Manage upgrades centrally to minimize disruption.
• Tackle bigger problems without breaking the bank: Create custom resource profiles to control access to CPU, memory, and GPUs for different projects or users. Tap the right resources for the job whether on-premises or in the cloud. Handle teams of any size, up to thousands of users via Kubernetes or Slurm.