Smarter hyperparameter optimization for machine learning teams. Find better configurations faster, with less experimentation.
Black-box optimization is expensive and slow without direction. Hyper Parameter Oasis proposes configurations informed by the full shape of past runs — not just final outcomes — so you spend less time on dead ends.
Proposals are informed by the full shape of past runs — not just final outcomes — so you spend less time on dead ends.
The optimization engine improves as more experiments are logged. Within a project, later runs benefit from earlier ones. Across projects in the same organization, patterns compound — so a second project starts ahead of where the first one did.
Request a configuration, run your process, report the results back. Works with any framework, language, or infrastructure.
Hyper Parameter Oasis proposes numerical configurations and records structured results. Every run is yours to execute, interpret, and act on.
Currently in beta. The service is provided as-is and may change as we develop the platform.