Modeling is messy.

Clean it up with a few lines of code.

With our API, you can now track runs, visualize training curves and optimize hyperparameters in a single place with a dashboard of insights every step of the way. We brought an intelligent approach to hyperparameter optimization and now we are doing the same with experiment management – whether you’re training, tuning, or optimizing your model.

Join the private beta to receive free access to this new functionality, and, for a limited time, our full product – including our flagship hyperparameter optimization solution. 

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.
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

Seamless 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.

Interested in learning more?

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 this video

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 our 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