Improve ML models 100x faster

SigOpt’s API tunes your model’s parameters through state-of-the-art Bayesian optimization.

  • Exponentially faster and more accurate than grid search. Faster, more stable, and easier to use than open source solutions.
  • Extracts additional revenue and performance left on the table by conventional tuning.
Demonstration of SigOpt for Bond Trading Model Optimization

Optimizing in-production models for

What is SigOpt?

SigOpt automates the tuning of your model’s hyper, feature, and architecture parameters. If you’re not optimizing them, you’re forsaking significant performance and revenue gains.

Modelers often overlook these optimizations because traditional approaches like manual, grid, and random search are time-consuming and produce subpar results.

Let SigOpt modernize your workflow so you can focus on what you’re best at: designing your model and understanding your data.

Improvement
+287%
Best Value
1.22
After 20 observations
Current Best Parameters
NameOptimal Value
x0.2063
y0.2075

ML, Trading, and Banking

Generate previously-unattainable optimization in mature industries where incremental gains have enormous impact.

Machine Learning

SigOpt works for all ML models.
Gradient boosting, neural networks, support vector machines, and more.
Read the docs.
Watch the video.

Algorithmic Trading

Find alpha hidden in your models.
No more manual tuning; get to market quicker. Plus, keep your model private.
Watch the video.

Banking and Insurance

SigOpt doesn’t break compliance.
We’re a black box only submit your anonymized inputs/outputs — keep your model private.

“For us, parameter optimization was always a secondary task, since we couldn’t dedicate the time or resources to it, and using SigOpt allows us to automate unlocking additional performance. They have an easy integration and useful web UI for tracking our models, but most importantly we saw performance improvements to the models optimized by SigOpt.”

– Ondrej Linda, PhD, Director of Data Science @ Hotwire

How SigOpt Works

1  Provide parameters

Ping our API with your model’s parameters. We don’t need the model itself. You keep it private.

2 Use our values

Our API suggests new values for these parameters. Use them to evaluate your model within your current infrastructure.

3 Send model output

We use your model’s output to calculate the next best configuration.

4 Repeat until optimized

You’ll reach optimal values up to 100x faster than other methods.

Works with every model
Integrates with every platform

Faster Than Everything Else

The world’s most efficient Bayesian optimization

We outperform traditional and alternative Bayesian techniques on a collection of benchmarks and real-world problems. We also outperform MOE, spearmint, SMAC, and hyperopt in a wide variety of problems.

Those tools usually represent a single optimization approach and are often too brittle for production. SigOpt is an ensemble of state-of-the-art, proprietary optimization strategies.

It’s why Huawei, Prudential, and MIT rely on us.

Read our peer reviewed comparison from ICML 2016 →

“SigOpt typically discovers a higher global maximum 10x faster than tuned grid-search.”

– Justin Lent, Director of Hedge Fund Development @ Quantopian

Company

Scott Clark, PhD

Co-founder and CEO

Scott Clark is an industry leader in the Bayesian optimization of machine learning models. He led the academic research behind Yelp’s Ad Targeting team. At Yelp, he was also responsible for developing the Dataset Challenge and open-sourcing the breakthrough MOE optimization library.

Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell. He also holds BS degrees in Mathematics, Physics, and Computational Physics from OSU.

Significantly improve all your models

Nothing outperforms SigOpt. Try the real-time demo.

Real-time Demo 

Or get a demo from the team:

Book Call 
With a university? Get SigOpt for Education!