Skip to main content

Concepts

This section covers the core concepts for working with PaletteAI. Whether you are a platform engineer setting up infrastructure or a data scientist deploying workloads, these concepts explain how PaletteAI organizes resources and manages AI/ML deployments.

Organization and Access​

PaletteAI uses a hierarchical structure to organize teams and control access to resources.

  • Tenants and Projects - Organizational hierarchy for multi-tenancy, GPU quotas, and team access control.
  • Settings - External integrations (Palette API credentials) and namespace-scoped configuration.
  • Roles and Permissions - RBAC roles automatically created for Tenants and Projects.

Compute Resources​

These resources define where your AI/ML applications run.

  • Compute - Discovers available machines for cluster provisioning.
  • Compute Config - Default settings for cluster deployment (networking, SSH, node configurations).
  • Compute Pools - Kubernetes clusters where applications run (dedicated or shared modes).

Workload Resources​

These resources define what gets deployed and how workloads are configured.

  • Workloads - Workloads, WorkloadProfiles, and WorkloadDeployments
  • Definitions - Components, Traits, and Policies that compose workloads.
  • Variables - User-defined configuration values for templating.
  • Environments - Placement policies that control which clusters receive workloads.

Deployments​

These resources package and deploy applications to your infrastructure.

  • Profile Bundles - Reusable packages combining Palette Cluster Profiles and PaletteAI Workload Profiles.
  • App Deployments - Deploy AI/ML applications to Compute Pools using Profile Bundles.

Deployment Flow​

A typical deployment involves these concepts working together:

  1. Platform setup - A platform engineer creates a Tenant with Settings for Palette integration
  2. Project creation - Teams get Projects with GPU quotas and role-based access
  3. Infrastructure provisioning - Compute discovers available machines; Compute Pools provision Kubernetes clusters
  4. Application packaging - Profile Bundles package infrastructure and application configurations
  5. Deployment - Data scientists create App Deployments using Profile Bundles on Compute Pools

For a deeper look at the system architecture, refer to the Architecture guide.