Machine Learning
and Simulation

With SigOpt, data scientists and machine learning engineers can build better models with less trial and error.

SigOpt increases model accuracy and accelerates hyperparameter tuning, using an ensemble of methods from current optimization research.

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Machine Learning

Manufacturing and
Process Engineering

From aerospace simulation to artificial egg whites, SigOpt helps scientists and engineers optimize the parameters of their manufacturing and industrial processes to minimize trial and error.

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Manufacturing and Process Engineering

Bayesian Optimization methods may be the wave of the future. Nothing like arriving at your answers faster, whether positive or negative—innovate even faster when discovering positive results, or just fail fast and move on more rapidly with your research!”
Justin Lent
Director, Hedge Fund Development

“… users will be able to quickly optimize their projects with better results …”

“SigOpt's promise is to optimize everything ... cutting the need for manual (and costly) trial and error.”

“SigOpt can be used to hone something as commonplace as packaged foods … or as scientifically advanced as medical devices, drugs and jet engines.”

“SigOpt lets our users efficiently tune their simulations, getting better results faster than traditional methods, allowing them to leverage Rescale for even more complex models while saving both time and money.”
Adam McKenzie

“… this is “democratizing” optimization making it accessible to a wider range of teams.”

“SigOpt is the future of scientific experimentation.”
– George Bonaci
Cofounder & VP Product

“SigOpt helps us optimize the financial models in our funds management platform. The API is convenient and easy to set up. SigOpt gets us better results, optimizing 15 times faster than before. Now, we’re happy to focus on other matters, because SigOpt saves us so much time.”
Max Logunov, PhD
Developer, Optimus Socially Responsible Investments LTD


SigOpt's REST API integrates into any machine learning workflow.

Instantiate SigOpt quickly with SigOpt's Python client.

SigOpt's R client makes offline tuning easy.

SigOpt's scikit-learn package can tune a model in one line of code.

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SigOpt makes it simple to tune parameters to maximize model accuracy, revenue, and other objectives