Accelerate your Machine Learning

A simple API for fast, optimal parameter tuning. A state-of-the-art Bayesian optimization ensemble.

Faster, Better, Cheaper Machine Learning

Machine learning models drive recommendations, advertising systems, predictive user segmentation, and fraud detection. SigOpt automates hyperparameter and feature parameter tuning to get the best performing model into production, up to 10x faster and cheaper than traditional methods.

Easy Management and Integration

Your models fly in and out of production and the parameters that drive them are often lost in configuration files. SigOpt provides a consistent interface, a parallel distributed scheduler for model tuning, and integrates with Python, Spark, TensorFlow, Neon, R, Java, and more.

Google and Netflix tune their models. Why don’t you?

Your machine learning models are critical to your business. You don’t put code into production without code reviews, so don't put models into production without tuning their parameters.

OptimoRoute develops routing algorithms for the logistics industry. OptimoRoute uses SigOpt to maximize the performance of their algorithms by tuning the hyperparameters, delivering value to the bottom line while keeping the underlying algorithms and IP safe.

“Our vehicle routing optimization algorithm uses 20+ hyperparameters that define how it explores the huge combinatorial space of this NP-complete problem. In a few weeks, SigOpt saved us months of a single engineers' time to yield a comparable performance improvement. Whenever we change our algorithm it makes us these kind of savings all over again. It's like having another engineer onboard.” - Marin Saric, OptimoRoute

SigOpt Machine Learning Examples

See SigOpt in action including deep learning, unsupervised learning with XGBoost, and logistic regression.

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