Our research team is constantly developing new optimization techniques for real-world problems.

Our team’s peer-reviewed research.

Research from our academic users.

Use SigOpt for reproducible research for free

Learn more about optimization and how SigOpt augments practitioners in the modeling workflow.

Industry trends and best practices.

Research and product innovation.

Decks for our talks, demos, webinars.

Recordings of our talks, demos, webinars.

Our mission is to accelerate and amplify the impact of modelers everywhere.

Meet the people behind SigOpt.

Apply to join our world-class team.

Read about our work in the news.

Check us out at upcoming events.

Community
Discuss, inquire, and discover with other practitioners.
Contact Us

All Model Types

Filter by type:

Experimenting with AI Optimizations - CitC Episode 250

Kisaco Research designates SigOpt as a Leader in AI Software Optimization

SigOpt Overview on Product, Features, and Use Cases

Webinar in review: MLOps to reduce friction, technical debt, and modeling

Model Optimization at the Center of MLOps

A Comparison of Bayesian Packages for Hyperparameter Optimization

Keeping track of it all: recording and organizing model training runs

SigOpt Webinar: Introducing Metric Management

Applying Prior Beliefs in SigOpt

Metric Management, a Comprehensive Stack to Track and Adapt Metrics to Fit Your Business

Metric Constraints help you closely align your models with your business objectives

Tuning for Systematic Trading: Intuition behind Bayesian optimization with and without multiple metrics

Efficient Rollout Strategies for Bayesian Optimization

Our new SigOpt user experience puts insights at your fingertips

Automating model tuning and hyperparameter optimization with SigOpt

Modeling at Scale: SigOpt at TWIMLcon 2019

Tuning 2.0: Advanced Optimization Techniques Webinar Recording

Tuning 2.0: Advanced Optimization Techniques Webinar

SigOpt at Ai4 Finance: Modeling at Scale

SigOpt at Ai4 Finance: Modeling at Scale

Interactive Tradeoffs Between Competing Offline Metrics with Bayesian Optimization

Number of AI Projects Set to Explode, But Challenges Persist

AI's carbon footprint will be an issue for enterprises

SigOpt at Uber Science Symposium: Exploring the spectrum of black-box optimization through customer engagements

SigOpt at GTC: Reducing Operational Barriers to Optimization

AI in the enterprise: 8 myths, debunked

Modeling at scale in systematic trading

SigOpt Webinar: Modeling at Scale in Systematic Trading

Best Practices for Scaling Modeling Platforms

SigOpt at O'Reilly: Best Practices for Scaling Modeling Platforms

Why Two Sigma is using SigOpt for Automated Parameter Tuning

Should you build or buy your data science platform?

SigOpt at GTC: Accelerating Model Development by Reducing Operational Barriers

SigOpt Partners with Two Sigma

AI vs. machine learning: What's the difference?

How to select an AI pilot project: 5 criteria

AI 100: The Artificial Intelligence Startups Redefining Industries

CB Insights: Here are the top 100 AI companies in the world

Enterprises are White Knights in Battle to Avoid AI Winter

Interview with Nick Payton, B2B Marketing Leader at SigOpt

“Above the Trend Line” – Your Industry Rumor Central for 1/14/2019

SigOpt at Ai4 Healthcare: Lessons for an Enterprise Approach to Model Optimization

Let's talk Bayesian optimization

Four ways artificial intelligence can impact the workplace and society

How machine learning will affect IT operations and careers

Tutorials in Optimization Research: Bayesian Optimization

CNBC unveils its annual list of 100 promising start-ups to watch

On the Radar: SigOpt for machine learning algorithm optimization

The Optimization Bottleneck

Four simple steps to implement model tuning

Optimization: The Secret Weapon of AI Modeling at Scale

AWS PrivateLink Now Supports Non-Amazon Services

21 Books Highly Recommended by Successful Executives

Uncertainty and How it Impacts Modeling, Optimization, and Metrics

Common Problems in Hyperparameter Optimization

Using Optimal Learning to Tune Deep Learning Pipelines

Using Bayesian Optimization to Simultaneously Tune Multiple Metrics

Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration

Two Sigma & SigOpt Discussion on Modeling Best Practices

SigOpt for Machine Learning and AI

SigOpt for Hedge Funds

SigOpt Automated Model Tuning: Product Overview and Analysis

SigOpt is Now Available on AWS Marketplace

Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration

Five Attributes of Artificial Intelligence Leaders

Uncertainty and How it Impacts Modeling, Optimization and Metrics

The insideBIGDATA IMPACT 50 List for Q3 2018

On the Radar: SigOpt for machine learning algorithm optimization

Metis: Robustly Optimizing Tail Latencies of Cloud Systems

Intel AI Developer Conference 2018

Odds & Ends: Amazon’s AI Scare, Encrypted Phones, A ‘Lost’-Inspired Startup

Roadblocks to Automated Testing

Model Optimization with Competing Objectives

How AI Is Transforming Financial Services

SigOpt Within In-Q-Tel’s Parameters

In-Q-Tel invests in SigOpt to help U.S. intelligence agencies optimize AI

Sequential Preference-Based Optimization

5 Questions with SigOpt’s Scott Clark

Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

Practical Bayesian optimization in the presence of outliers

On Data and Data Scientists in the Age of AI

AI, from ‘Toy’ Problems to Practical Application

AWS Launches New Cybersecurity Services

How Building the Perfect AI Pipeline Is Like Brewing the Perfect Shot of Espresso

Filtering Outliers in Bayesian Optimization

Policy Search using Robust Bayesian Optimization

A Strategy for Ranking Optimization Methods using Multiple Criteria

Adaptive Sequential Experimentation Techniques for A/B Testing and Model Tuning

Voices in AI

Tips and Techniques for Hyperparameter Optimization

Bayesian Optimization for Hyperparameter Tuning

Bayesian Optimization for Hyperparameter Tuning with Scott Clark

SigOpt CEO on the art of tuning AI models and doing ‘optimization as a service’

Active Preference Learning for Personalized Portfolio Construction

Common Problems in Hyperparameter Optimization

Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation

This ex-Yelp engineer just raised $6 million to help make tastier beer

Optimization startup SigOpt raises $6.6M

SigOpt

SigOpt Raises $6.6 Million in Series A Funding

Andreessen Horowitz backs $6.6 million round for optimization startup SigOpt

Evaluation System for a Bayesian Optimization Service

A Stratified Analysis of Bayesian Optimization Methods

SigOpt Raises $2 Million to ‘Optimize Anything’ From Jet Engines to Snacks

YC-Backed SigOpt Helps Customers Optimize Everything From Online Ads To Shaving Cream

Experiment, Suggestion, Observation: How We Named Our HPO API Objects

SigOpt at NeurIPS 2018

The SigOpt Academic Program

Multimetric Updates in the Experiment Insights Dashboard

Uncertainty 3: Balancing Multiple Metrics with Uncertainty

Uncertainty 2: Bayesian Optimization with Uncertainty

Uncertainty 1: Modeling with Uncertainty

New Advanced Feature: Constraints

Circulant Binary Embeddings

SigOpt Winter Update

SigOpt Working with AWS to Provide PrivateLink Support for Customers on AWS

SigOpt Fall Update

SigOpt is Now Available on AWS Marketplace

At SigOpt, We're Stronger as a Team

SigOpt Wins Barclays 2017 Open Innovation Challenge

Covariance Kernels for Avoiding Boundaries

SigOpt Named A Gartner "Cool Vendor" in AI Core Technologies

Expected Improvement vs. Knowledge Gradient

Clustering Applied to Acquisition Functions

Common Problems in Hyperparameter Optimization

Announcing SigOpt Organizations

Building a Better Mousetrap via Multicriteria Bayesian Optimization

Solving Common Issues in Distributed Hyperparameter Optimization

Intro to Multicriteria Optimization

We Raised $6.6 Million To Amplify Your Research

Breaking Free of the Grid

Evaluating Hyperparameter Optimization Strategies

Dealing with Troublesome Metrics

Lessons from a RESTful API Redesign

Intuition behind Gaussian Processes

Profile Likelihood vs. Kriging Variance

Likelihood for Gaussian Processes

Automatically Tuning Text Classifiers

Approximation of Data

Intuition Behind Covariance Kernels

We Raised Seed Financing from Andreessen Horowitz

Picking the Right Metric