Streamlining User Access with Determined AI's MLDE RBAC

Streamlining User Access with Determined AI's MLDE RBAC

Table of Contents:

  1. Introduction
  2. Who is Determined AI?
  3. The Enterprise Version of HPE Machine Learning Development Environment (MLDE)
  4. Role-Based Access Control (RBAC)
    1. Users and Groups in MLDE
    2. Permissions
    3. Roles
    4. Scopes
  5. Demonstrating RBAC in MLDE
    1. Creating Users and Groups
    2. Assigning Roles and Permissions
    3. Managing Workspaces and Projects
    4. Limiting Access for Viewers
    5. Registering Checkpoints in the Model Registry
  6. Built-in Roles and Custom Roles
  7. The Future of RBAC in MLDE
  8. Conclusion

🚀 Article: Understanding Role-Based Access Control in Determined AI's MLDE

Role-Based Access Control (RBAC) is an essential feature in modern software development environments, providing the necessary framework to manage user access and permissions effectively. In this article, we will explore how Determined AI's Machine Learning Development Environment (MLDE) incorporates RBAC to streamline user management and improve security.

Introduction

Determined AI is a leading provider of machine learning infrastructure and solutions, empowering organizations to harness the full potential of artificial intelligence. Their Enterprise version, known as the HPE Machine Learning Development Environment (MLDE), offers a comprehensive platform for managing and deploying machine learning models at scale.

Who is Determined AI?

Determined AI is a renowned developer advocate in the AI community. The company's mission is to simplify the path to AI success by providing cutting-edge tools and resources to data scientists, engineers, and researchers. With a team of industry experts and a deep understanding of the challenges faced by AI practitioners, Determined AI aims to revolutionize the way machine learning is developed and deployed.

The Enterprise Version of HPE MLDE

The Enterprise version of HPE MLDE is a powerful machine learning development environment designed specifically for enterprise-grade use cases. It offers enhanced features and capabilities compared to the open-source version of Determined AI. With a focus on scalability, security, and collaboration, HPE MLDE provides a robust framework for teams to collaborate on machine learning projects.

Role-Based Access Control (RBAC)

RBAC is a fundamental concept in access control management, enabling organizations to define and enforce granular user permissions based on their roles within the system. In the context of MLDE, RBAC allows administrators to control who can do what and where within the platform.

Users and Groups in MLDE

To implement RBAC in MLDE, the system recognizes two main entities: users and groups. Users represent individual team members, while groups provide a way to organize users with similar access requirements. MLDE allows users and groups to be created manually or provisioned through an identity provider like Okta using SCIM.

Permissions

Permissions in MLDE define what actions users are allowed to perform within the platform. Examples of permissions include creating workspaces, editing experiment metadata, and creating experiments. Users are granted permissions based on the roles assigned to them.

Roles

Roles in MLDE are pre-defined bundles of permissions that can be assigned to users and groups. The available roles in MLDE include Cluster Admin, Workspace Admin, Editor, Viewer, Workspace Creator, and Model Registry Viewer. These roles control the level of access a user or group has within the platform.

Scopes

In RBAC, scopes define the level at which roles and permissions are applied. In MLDE, scopes are defined at the workspace level. Workspaces serve as the organizational units within MLDE, allowing users and groups to collaboratively work on specific projects. Scopes can be either "Global," applying to all workspaces, or "Single Workspace," applying to a specific workspace.

Demonstrating RBAC in MLDE

To better understand RBAC in MLDE, let's walk through a demonstration of creating users, assigning roles, and managing permissions within MLDE.

Creating Users and Groups

In MLDE, users and groups can be created through the web UI or using the CLI. Users can be manually created or provisioned from an identity provider like Okta. Similarly, groups can be created and managed, allowing administrators to organize users based on their access requirements.

Assigning Roles and Permissions

Once users and groups are created, roles can be assigned to them depending on their responsibilities and access needs. Roles bundle specific permissions together and can be applied globally or within specific workspaces. Users can be assigned multiple roles based on their requirements.

Managing Workspaces and Projects

Workspaces serve as the primary organizational units in MLDE, providing a dedicated space for users and groups to collaborate on machine learning projects. Within workspaces, users can create projects, store artifacts such as experiments and models, and manage access control settings.

Limiting Access for Viewers

RBAC allows organizations to limit access for viewers, providing read-only permissions for specific workspaces or projects. This enables collaboration while ensuring sensitive information remains secure.

Registering Checkpoints in the Model Registry

MLDE features a Model Registry where users can register checkpoints, representing different versions of their trained machine learning models. RBAC allows administrators to control who can register checkpoints and access the Model Registry.

Built-in Roles and Custom Roles

MLDE offers several built-in roles to cater to different user roles and responsibilities. However, MLDE also allows organizations to define custom roles by bundling specific permissions together. Custom roles enable administrators to tailor access control settings according to their unique requirements.

The Future of RBAC in MLDE

Determined AI continues to enhance and refine RBAC capabilities in MLDE. In the future, MLDE plans to extend RBAC to the project level, allowing organizations to apply permissions at a more granular level. This would further enhance collaboration and access control within MLDE.

Conclusion

RBAC plays a crucial role in managing user access and permissions within Determined AI's MLDE. With RBAC, organizations gain greater control over who can do what and where within the platform, ensuring security and collaboration between teams. As MLDE evolves, RBAC will continue to be an integral part of its feature set, offering enhanced access control to enterprises deploying machine learning models at scale.


Highlights:

  • Determined AI's Machine Learning Development Environment (MLDE) offers a robust platform for managing and deploying machine learning models at scale.
  • Role-Based Access Control (RBAC) is crucial in MLDE, allowing organizations to effectively manage user access and permissions.
  • RBAC in MLDE involves users, groups, permissions, roles, and scopes.
  • MLDE provides a web UI and CLI for creating users and groups, assigning roles and permissions, and managing workspaces and projects.
  • Viewers in MLDE have limited access, ensuring that sensitive information remains secure.
  • MLDE offers built-in roles and the flexibility to define custom roles to tailor access control settings.
  • Determined AI plans to extend RBAC to the project level in future updates of MLDE.

FAQ:

Q: Can users define their own custom roles in MLDE? A: Currently, MLDE only supports the use of built-in roles. However, the option to create custom roles is being considered for future updates.

Q: How can I get help if I encounter any issues or need assistance with MLDE? A: If you need assistance, you can post your questions in the Determined AI Community Slack workspace, specifically the hackathon channel. The dedicated support team will provide prompt assistance to resolve any issues you may be facing.

Q: What are the future plans for RBAC in MLDE? A: MLDE aims to extend RBAC to the project level, allowing organizations to apply permissions at a more granular level. This enhancement will further enhance collaboration and access control within MLDE.

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