AI-powered interface for Kubeflow Training via MCP, enabling AI assistants to manage distributed training jobs, fine-tune LLMs, and monitor workloads on Kubernetes through natural language.
An MCP server that enables AI agents to observe and interact with trackio experiment tracking, providing tools for managing ML experiments through natural language.
Exposes TensorBoard experiment data through a standardized MCP API, enabling AI coding agents to query and analyze scalars, tensors, histograms, distributions, and images from ML experiment logs.
An MCP server that enables interaction with property-driven machine learning experiment pipelines, including listing experiments, retrieving configs, comparing runs, and executing experiments with approval gates and full tracing.
Enables Kubernetes-native management of agent/model workloads via MCP tools, including fleet status, workload lifecycle, and boot orchestration for AI workflows.