Build the best models

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.”

George Hoyem
Managing Partner
In-Q-Tel

Model-Analysis-Selection

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.”

Matt Adereth
Managing Director
Two Sigma

Production-and-Deployment

Accelerate Model Development

“Integrating SigOpt into our modeling platform empowers our team to more efficiently experiment, optimize and, ultimately, model at scale.”

Peter Welinder
Research Scientist
OpenAI

Empowering Leading AI Firms Worldwide

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  • Training Run Detail Page
  • Table of Project Training Runs
  • Project Analysis Page
  • Checkpoints Chart in the Project Analysis Page
  • Example Checkpoint Code
  • Optimize your model in Jupyter with only one line of code
  • See a comparison of ongoing and past experiments.

Take a Tour of an Example Deep Learning Model

Step through how to integrate SigOpt Experiment Management in a Jupyter Notebook and how to use the Web interface.

Tracks and Runs in Model Management

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.

Visualization of Runs in Metric Management
Training and Tuning in Metric Management

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 ML 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.

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Runs Full

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.