Automate Optimization to Accelerate
and Amplify Model Development
SigOpt is a standardized, scalable, enterprise-grade optimization platform and API designed to unlock the potential of your modeling pipelines.
Our black-box hyperparameter optimization solution automates model tuning to accelerate the model development process and amplify the impact of models in production at scale. This process empowers our customers to generate more high-performing models in production. And with more models in production, they earn a higher return on their modeling investment.
“We can keep our experts focused on the tasks core to our business, and entrust the SigOpt platform to find the optimal hyperparameter configurations for our models, irrespective of the data type and model type.”
– Deep Learning Engineer
“SigOpt has helped us solve an optimization problem that was too challenging for traditional approaches. SigOpt has powered a marketing allocation simulator in a way that has given both us and our clients a competitive advantage.”
– Head of Data Science
“SigOpt has a highly effective product that we’ve used in a variety of ways to enhance our workflow and research pipeline.”
– Head of Machine Learning
Applying optimization techniques to enterprise modeling use cases comes with its own host of unique challenges. Our research team is passionate about evolving our optimization solution to address these challenges so our customers can trust the performance of their models in production and at scale. In the process, our customers often abandon grid search, random search, and open source Bayesian optimization.
Whether utilizing our leading Optimization Engine or advanced features like Multitask Optimization, our customers tune their models much faster than when using alternative methods. This becomes particularly important as teams increase the complexity or dimensionality of their models. Explore use cases in which SigOpt tunes models 10x faster than other methods.
SigOpt significantly increases computational efficiency with an ensemble of Bayesian and global optimization algorithms that are designed to efficiently explore and exploit any parameter space. When combined with leading AI hardware, this approach results in enormous cost savings that scale with modeling over time. Learn how AWS, NVIDIA and SigOpt efficiently scale model training and tuning.
There is no free lunch, but SigOpt consistently outperforms grid, random, and other Bayesian search methods across a wide cross-section of problems. Though the primary benefit of SigOpt is that it can efficiently optimize any model, it most often delivers better performance along the way. Learn how we compare in a stratified analysis of Bayesian optimization methods.
Explore applied model optimization research, machine learning market trends and real-world enterprise use cases for hyperparameter optimization