Modern engineering and scientific problems face a common problem –– how to efficiently find promising candidate solutions from a large space of possible choices of configurations. This complex configuration space could be represented by hyperparameters of a machine learning model, environment configuration variables of a hardware system, resource allocation of a cluster, or design parameters of a simulated materials.
Searching and tuning these configurations are often resource/time intensive. SigOpt is an optimization platform that automates this process. It streamlines your process to get to desirable results, and also encourages you to experiment with your problem.
SigOpt is a platform designed for the sample-efficient search of desirable outcomes in a complex configuration space while considering one or multiple objective metrics.
Multiple objectives - SigOpt supports multiobjective and constrained optimization.
Complex search space - SigOpt supports, continuous, integer, discrete, categorical variables, parameter constraints, and conditional dependencies.
Flexible API - The core SigOpt API follows the RESTful design principle.
Interactive User Experience - Sigopt has a web dashboard that shows an overview of experiments and allows for exploration.
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