AIrun

AIrun is a platform for deploying and managing artificial intelligence workflows across heterogeneous compute environments. It provides orchestration, model lifecycle management, data lineage, and observability from development to production, emphasizing reproducibility, portability, and scalable resource use.

AIrun began as an open-source project in the early 2020s and reached its first widely used 1.0 release a couple years later. It has since evolved through community and vendor contributions, with ongoing emphasis on standard interfaces and modular components.

AIrun architecture centers on four core components: a scheduler that assigns tasks across compute resources; a data plane that streams and stores data for training and inference; a model registry for versioned artifacts; and a policy engine for access control and compliance. The platform runs on Kubernetes and supports major cloud runtimes, with configuration expressed in declarative files. Security features include role-based access control, audit logs, and encrypted data channels.

Key features include workflow orchestration, experiment tracking, a model registry, multi-tenant support, distributed training and inference, autoscaling, and cost monitoring. It offers support for hardware accelerators such as GPUs and, where available, specialized accelerators. It integrates with common ML tooling and provides APIs for custom extensions.

Use cases span research and development pipelines, MLops for production models, edge deployments, and education/training environments. Administrators use it to reproduce experiments, compare model variants, and manage governance across teams.

Reception has been mixed: praised for improving reproducibility and governance, while some users report a steep learning curve and complexity for small teams. The project maintains an active community with regular releases and documentation.

Related projects include MLflow and Kubeflow, which address similar problems in experiment tracking and pipeline orchestration.