Some modeling problems are best solved through graph form—like identifying money laundering. In this video, PayPal shares how they approach Graph Neural Networks for detecting fraud: bit.ly/3NC66Vb

How are you using SigOpt open source? With our new open source offering, teams can run their own self-hosted servers—meaning your data doesn't leave your server. Learn more: sigopt.org/#self-hosted

Did you know that you can bring your own optimizer to SigOpt? Check out our quick-start guide to using your own optimizer and storing your progress in SigOpt: bit.ly/44rDoNs

Constraint Active Search offers an alternative to working with the Pareto efficient frontier, making it an ideal approach for material sciences and production. In this video, Gustavo Malkomes shares some of SigOpt's latest research on CAS: bit.ly/3zo9s5O

SigOpt offers two API modules: Core Module and AI Module. Not sure which one is right for your #ML project? Check out our guide here: bit.ly/3KewRid

Our brains only use about 30-40 watts of power, yet are more powerful than neural networks – which take extensive energy to run. In this interview, learn how Numenta 🧠 is building neural networks inspired by the sparsity of the human brain: bit.ly/46XEToE

See how SigOpt stacks up. In this short video, Associate Professor Paul Leu walks through his test comparing two popular optimization techniques using SigOpt's intelligent experimentation platform to empirically determine the best-performing algorithm: bit.ly/3K5GC1A

Parameters are a crucial part of every experiment, defining the domain to be searched – which is why SigOpt supports double, integer, and categorical parameter types. Learn more about SigOpt's tools to construct a domain for your specific modeling problem: bit.ly/46WguA4

“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 Learn how SigOpt can help you amplify the impact of your models: sigopt.com

GNNs can powerfully model tasks to capture a dataset’s natural graph representation, but they are very memory- and compute-hungry. Learn how leaders from PayPal, Intel, and Amazon Web Services are optimizing and scaling GNNs: bit.ly/44NFXdW

An All Constraints experiment can help modelers study which parameter regions consistently yield high-performing models. Learn how to use this advanced experimentation technique using SigOpt: bit.ly/3qmpZI5

What is the DLRM model? Intel AI Principal Engineer Ke Ding provides an overview of what this model is and how to use it in this short video: bit.ly/3OF0Th9

“Integrating SigOpt into our modeling platform empowers our team to more efficiently experiment, optimize, and ultimately, model at scale.” – Peter Welinder, Research Scientist OpenAI Learn how SigOpt helps teams accelerate their model development: sigopt.com

Implement SigOpt with just a few lines of code. Instrument your model code to track runs and model artifacts—here's how to get started: bit.ly/45y8d3D

Modelers can use SigOpt for nearly anything: #DeepLearning, #MachineLearning, or even Airplane Design. Check out our sample use cases for more examples of how to use SigOpt for your business: bit.ly/45iqW3t

What is distillation? In this short video, Meghana Ravikumar explains how distillation transfers the knowledge from a large model to a much smaller one, using BERT as an example: bit.ly/3hNdQDz