KubeA

KubeA is a hypothetical open-source platform designed to extend Kubernetes for AI workloads, combining container orchestration with AI lifecycle management. It provides declarative abstractions for training, inference, and data processing on standard Kubernetes clusters.

Core components include AIJob and AIExperiment custom resources, a Model Registry, an AI Scheduler, an AI Controller, and Inference Services. The platform uses Kubernetes API machinery to schedule pods and Jobs, with GPU/TPU aware scheduling, namespace isolation, RBAC, and audit trails. Data connectors link to storage, streaming services, and data warehouses, while policy controllers enforce resource and security constraints.

Key features include declarative AI workload definitions, automatic resource provisioning, distributed training support, model versioning and lineage, experiment tracking, autoscaling, and edge deployment options.

Typical workflow: define an AIJob resource outlining training or inference tasks, datasets, and compute requirements; submit to the cluster; the AI Scheduler allocates resources and launches tasks; progress and metrics are reported to the AI Controller and stored in the Model Registry; successful models can be deployed via Inference Services with monitoring.

KubeA integrates with Kubeflow components, ML pipelines, and data orchestration tools, supporting plug-ins and standard Kubernetes practices. The project targets ML engineers, data scientists, and platform teams seeking consistent AI workload infrastructure.

Origins trace to a community effort in the AI Kubernetes space, with governance by a steering committee and open contribution processes. Development occurs in public repositories, with regular releases and transparent decision-making.

See also: Kubernetes, Kubeflow, MLflow.