An AIWorkload is the custom resource that represents an App Deployment or Model Deployment on the PaletteAI hub cluster. When you complete the App Deployment or Model Deployment workflow, PaletteAI creates an AIWorkload resource in your Project namespace. The resource captures everything the deployment needs: the Profile Bundles to deploy, the workloads to generate from them, and the Compute Pool that runs them.
An App Deployment represents an AI/ML application deployed using a Profile Bundle; the Profile Bundle must contain a Workload Profile with the type Application. An App Deployment is the primary method that data scientists and ML engineers use to deploy their workloads onto Compute Pools.
A Compute Pool is a group of shared Compute resources used to create Kubernetes clusters where your AI/ML applications run. In the hub-spoke architecture, each Compute Pool becomes a spoke cluster on which applications and models are deployed.
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.
Definitions is an umbrella term that encapsulates Components, Traits, and Policies. They are reusable building blocks that describe how to deploy Workloads.
An Environment is an abstraction that dictates what clusters a workload should be deployed to.
A Workload represents an application or task that runs on your infrastructure. PaletteAI creates Workloads from AIWorkload resources by using three building blocks called Definitions: