Blog | SigOpt

New Alpha Feature: Monitor Training Convergence

We are excited to release a new SigOpt feature into alpha testing: Training Monitor. This feature was designed to better empower our neural network developers.

SigOpt Partners with Two Sigma to Extend its Leadership in Model Experimentation and Optimization Solutions

SigOpt, Inc. (“SigOpt”), a leading provider of solutions that maximize the performance of machine learning, deep learning and simulation models, announced a strategic partnership with Two Sigma, a leading systematic investment manager.

In our previous post, we had discussed the challenge of climate change and the mitigation pathways outlined by the IPCC to curb global warming to 1.

Climate change is the environmental challenge of our generation.

The research team at SigOpt is fortunate to be part of an outstanding community of experts from around the world.

We are excited to release a new SigOpt feature into alpha testing: Training Monitor. This feature was designed to better empower our neural network developers.

Every year, the US National Science Foundation awards five year grants to outstanding tenure-track professors.

Machine learning infrastructure tools can help bridge the gap between the modeler and the cluster by inserting an abstraction layer between the model builder and any infrastructure tools used to communicate with the cluster.

We are pleased to announce the addition of Projects, a feature that allows you and your team to organize experiments for easier access, viewing, and sharing.

Using SigOpt’s technology we have devised a method to generate high fidelity simulations of biological processes.

We are excited to announce improvements to our experiment list and search functionality in the Experiment Insights dashboard.

As part of our mission to make hyperparameter optimization accessible to modelers everywhere, we want to share our naming choices with other developers of HPO APIs.

In this latest Academic Interview, Brady Neal from the Mila (Quebec Artificial Intelligence Institute) discuss the paper, “A Modern Take on the Bias-Variance Tradeoff in Neural Networks” and its implications for deep learning practitioners.

The NeurIPS conference is held in Montreal this year, where leaders in the community will present innovative research over the course of tutorials, main conference proceedings and workshops.

This post introduces the article A Nonstationary Designer Space-Time Kernel by Michael McCourt, Gregory Fasshauer, and David Kozak, appearing at the upcoming NeurIPS 2018 spatiotemporal modeling workshop.

Our research team at SigOpt has been very fortunate to be able to collaborate with outstanding researchers around the world, including through our internship program.

This post introduces the article Efficient Nonmyopic Batch Active Search by Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley and Roman Garnett, appearing in the upcoming NeurIPS 2018 proceedings.

This post introduces the article Automating Bayesian Optimization with Bayesian Optimization by Gustavo Malkomes and Roman Garnett, appearing in the upcoming NeurIPS 2018 proceedings.

If you are an academic working on peer-reviewed research and need parameter tuning, we would love to help supercharge your research with SigOpt.

This post is an interview with the DeepDIVA team from the University of Fribourg to learn more about their framework and what it means for the broader community.

We are excited today to announce improvements in analyzing and viewing Multimetric experiments with the Experiment Insights dashboard.

This is the third of three blog posts during which we explore the concept of uncertainty - or noise - and its implications for Bayesian optimization.

This is the second of three blog posts during which we explore the concept of uncertainty - or noise - and its implications for Bayesian optimization.

This is the first of three blog posts during which we explore the concept of uncertainty - or noise - and its implications for Bayesian optimization.

Competitions like International Conference on Machine Learning help explore the complexities of developing effective ML pipelines under severe time restrictions and without expert intuition regarding the desired or expected output.

SigOpt is constantly working to extend the capabilities of our platform by supporting different types of optimization problems.

The research team at SigOpt works to provide the best Bayesian optimization platform for our customers. In our spare time, we also engage in research projects in a variety of other fields.

We’re happy to announce a strategic investment and technology development agreement with In-Q-Tel (IQT).

Happy New Year! 2017 was a great year for SigOpt.

AWS PrivateLink is a new solution that will enable SigOpt to connect directly with any AWS customer that has an Amazon Virtual Private Cloud (VPC).

This is the first edition of our new quarterly newsletter. In these updates, we will discuss newly released features, showcase content we have produced, published, or been cited in, and share interesting machine learning research that our research team has found. We hope you find these valuable and informative!

Today we announce the availability of SigOpt on AWS Marketplace. Now, with a single click, data scientists and researchers can access SigOpt’s powerful optimization-as-a-service platform designed to automatically fine-tune their machine learning (ML) and artificial intelligence (AI) workloads.

No one person is going to have all of the answers, so we're focused on making ourselves stronger as a team.

Congratulations to the other innovative startups that were honored this year!

Here at SigOpt, Gaussian processes and reproducing kernel Hilbert spaces (RKHS) are important components of our Bayesian optimization methodology.

Today, we’re honored to share that Gartner has listed us in its Cool Vendor 2017 report for AI Core Technologies.

In this blog post, we discuss another ingredient of Bayesian optimization: the sampling policy (or acquisition function).

We have revisited one of the traditional acquisition functions of Bayesian optimization, the upper confidence bound and looked to a slightly different interpretation to better inform optimization of it. In our article, we present a number of examples of this clustering-guided UCB method being applied to various optimization problems.

In this blog post, we are going to show solutions to some of the most common problems we’ve seen people run into when implementing hyperparameter optimization.

We are excited to announce SigOpt Organizations, the next step in the evolution of our web dashboard.

In this post, we will show you how Bayesian optimization was able to dramatically improve the performance of a reinforcement learning algorithm in an AI challenge.

In this post, we want to analyze a more complex situation in which the parameters of a given model produce a random output, and our multicriteria problem involves maximizing the mean while minimizing the variance of that random variable.

SigOpt’s API for hyperparameter optimization leaves us well-positioned to build exciting features for anyone who wants to perform Bayesian hyperparameter optimization in parallel.

In this post we will discuss the topic of multicriteria optimization, when you need to optimize a model for more than a single metric, and how to use SigOpt to solve these problems.

In this post we will show how to tune an MLlib collaborative filtering pipeline using Bayesian optimization via SigOpt. Code examples from this post can be found on our github repo.

Today is a big day at SigOpt. Since the seed round we secured last year, we’ve continued to build toward our mission to ‘optimize everything,’ and are now helping dozens of companies amplify their research and development and drive business results with our cloud Bayesian optimization platform.

In this post, we review the history of some of the tools implemented within SigOpt, and then we discuss the original solution to this black box optimization problem, known as full factorial experiments or grid search. Finally, we compare both naive and intelligent grid-based strategies to the latest advances in Bayesian optimization, delivered via the SigOpt platform.

In this post, we will detail how to use SigOpt with the Nervana Cloud and show results on how SigOpt and Nervana are able to reproduce, and beat, the state of the art performance in two papers.

SigOpt provides customers the opportunity to build better machine learning and financial models by providing users a path to efficiently maximizing key metrics which define their success. In this post we demonstrate the relevance of model tuning on a basic prediction strategy for investing in bond futures.

This blog post provides solutions for comparing different optimization strategies for any optimization problem, using hyperparameter tuning as the motivating example.

At SigOpt, our goal is to help our customers build better models, simulations, and processes by maximizing key metrics for them.

In this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and TensorFlow to efficiently search for an optimal configuration of a convolutional neural network (CNN).

In this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and XGBoost to efficiently optimize an unsupervised learning algorithm’s hyperparameters to increase performance on a classification task.

When building the new and improved v1 API, we stepped back and chose to more closely adhere to RESTful architectural principles.

Is it possible to use optimized machine learning models to beat Vegas? The short answer is yes; read on to find out how.

SigOpt allows experts to build the next great model and apply their domain expertise instead of searching in the dark for the best experiment to run next. With SigOpt you can conquer this tedious, but necessary, element of development and unleash your experts on designing better products with less trial and error.

The first post in our SigOpt in Depth series which deals with more technical aspects of topics that appear in the SigOpt Fundamentals series.

Using the best approximation gives our customers the fastest path to optimal behavior, which minimizes the costs of experimentation.

In this first post on integrating SigOpt with machine learning frameworks, we’ll show you how to use SigOpt and scikit-learn to train and tune a model for text sentiment classification in under 50 lines of Python.

SigOpt provides customers the opportunity to define uncertainty with their observations and using this knowledge we can balance observed results against their variance to make predictions and identify the true behavior behind the uncertainty.

Gaussian processes are powerful because they allow you to exploit previous observations about a system to make informed, and provably optimal, predictions about unobserved behavior. They do this by defining an expected relationship between all possible situations; this relationship is called the covariance and is the topic of this post.

Rescale gives users the dynamic computational resources to run their simulations and SigOpt provides tools to optimize them. By using Rescale’s platform and SigOpt’s tuning, efficient cloud simulation is easier than ever.

When we created SigOpt, our mission was simple: optimize everything. What do we mean by that?

At SigOpt, we can help you raise this metric automatically and optimally whether you are optimizing an A/B test, machine learning system, or physical experiment.

SigOpt provides a simple API and web interface for quickly and easily leveraging cutting-edge optimization research to solve this problem for you.

Research & Company Blog

New Alpha Feature: Monitor Training Convergence

SigOpt Partners with Two Sigma to Extend its Leadership in Model Experimentation and Optimization Solutions