Augment Expert Productivity
“SigOpt offers an advanced and scalable solution capable of impacting the performance of any type of AI model. Whether working on simulations, reinforcement learning, deep neural networks, machine learning or anything in between, researchers can use SigOpt to track, analyze, and tune their models.”
Amplify Impact of Your Models
“We’ve integrated SigOpt’s optimization service and are now able to get better results faster and cheaper than any solution we’ve seen before.”
Accelerate Model Development
“Integrating SigOpt into our modeling platform empowers our team to more efficiently experiment, optimize and, ultimately, model at scale.”
Empowering Leading AI Firms Worldwide
Powering Leading Research
Backed by the World’s Top Investors
Partnered with Other AI Leaders
Globally Recognized and Award Winning
Implement SigOpt with just a few lines of code
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. To get started:
- Instrument your model code to track runs and model artifacts
- Automate sample-efficient hyperparameter optimization
- Utilize advanced experimentation features to get more out of your modeling
- Visualize training and tuning in our dashboard
- See a variety of use cases that may fit your current modeling project
Track & Organize Modeling Attributes
Modeling is messy and it can be hard to keep track of everything. With just a few lines of code, SigOpt tracks and organizes your training and tuning cycles, including: architectures, metrics, parameters, hyperparameters, code snapshots and the results of feature analysis, training runs or tuning jobs. Consider us your intern, sidekick, advisor, or all of the above.
Visualize & Compare Runs
Modeling is often about tradeoffs, but it’s hard to gather insights to properly evaluate these. Quickly gain intuition on your models and their performance with an API that automatically populates your dashboard with customizable visualizations and in-depth hyperparameter, metric and run insights as you train and tune.
Automate Training & Tuning
Transitioning between training and tuning can be expensive in time and resources, so many modelers leave tuning until the last mile. Our solution fully integrates automated hyperparameter tuning with training run tracking to make this process easy and accessible. And features like automated early stopping, highly customizable search spaces, multimetric optimization and multitask optimization make tuning useful for any model you are building.
Use SigOpt with any Library
Our solution is fully agnostic to modeling framework, compute stack, orchestration setup, or coding environment. And our back-end is designed to work equally well for startups with a few modelers and a few tuning jobs a month to modeling leaders with thousands of modelers and millions of concurrent tuning jobs per month.
Explore, Understand, Advance
We design software that sets modelers up for success. Use SigOpt to:
- Efficiently explore your problem space by comparing results from your model development process.
- Understand model performance with easy ways to visualize patterns in learning curves, assess parameter importance, and evaluate metric comparisons.
- Once you’re on to something promising, advance your model with easy hyperparameter tuning before taking it into production.
Review the docs
Learn how to use Runs API through our Python client in either command line interface or notebook environments, and how to view history, visualizations, and comparisons in our dashboard.
Watch our videos
View a use case walking through how to use SigOpt for training and tuning in a fraud detection case to explore the modeling problem, understand the model options, and advance the best model to production.
Read a blog post
Get a sense for the context around why our customers requested we build this feature, how it has already made an impact for users, and how we expect it will impact your modeling workflow.