MLX: Machine Learning Asset Catalog for Kubeflow and Tekton

MLX is a machine learning asset catalog and execution engine developed by the claimed-framework organization, with updates recorded through September 2025. It provides a structured repository for models, pipelines, and Jupyter notebooks specifically designed for Kubernetes and OpenShift environments.

Use Cases

• Deploying automated machine learning workflows using the kubeflow-pipeline definitions
• Organizing and versioning AI models within the mlx-catalog structure
• Integrating notebook-jupyter files into scalable Kubernetes-based execution environments

Strengths

• Apache-2.0 licensed for open-source use
• Dual support for Kubeflow and Tekton orchestration engines
• Native integration with OpenShift and Kubernetes infrastructure

Limitations

• Does not contain raw observational data, functioning instead as a metadata catalog
• Asset count and specific metadata fields are not publicly enumerated in the summary
• Requires existing Kubernetes or OpenShift infrastructure for asset execution

Provenance

Source

claimed-framework

Collection Method

Aggregated from machine learning development and orchestration tools

Freshness

Current as of September 2025.