SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure
*Performance: Accelerate hyperparameter optimization jobs by up to 10x faster than other intelligent optimization methods and up to 100x times faster than naive grid and random search
*Productivity: Reduce time to a viable model by 30% by automating tasks like hyperparameter tuning and logging, and providing more seamless opportunity for collaboration in our dashboard
*Efficiency: Scale across your full compute bandwidth seamlessly regardless of your infrastructure or the modeling library you are applying to save wall-clock time and reduce the number of training runs required to get to a viable performance threshold to save total compute required for your modeling project
*Portability: Use SigOpt across all environments, libraries, tasks without adjusting your workflow to avoid vendor lock-in and future-proof your modeling process.
*Experiment design: Define strategies for metrics, parameters, and compute that set training and optimization up for success.
*Model Exploration: Automatically track modeling artifacts during training runs, seamlessly apply active search to explore your parameter space, and visualize checkpoints, metrics, and parallel coordinates to deeply understand your model's behavior
*Model Optimization: Apply any optimizer of your choice within SigOpt or use SigOpt's proprietary optimizer for hyperparameter tuning, run in parallel to utilize your full compute, and utilize advanced research features to uncover novel insights on your modeling problem
*Dashboard: Log your full history of all runs and experiments, capture code snapshots, collaborate on modeling projects, manage user permissions, compare models, and visualize training curves.
*Open and Agnostic: Run SigOpt in any coding environment across any compute infrastructure on any modeling problem with any modeling library without adjusting your workflow.