Hello, modelers — we have an announcement to share. It’s time for SigOpt to wind down. After a good 9-year run, we are planning to shift our efforts internally.

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

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

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

“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

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

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

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 @awscloud are optimizing and scaling GNNs: bit.ly/44NFXdW

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

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

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

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

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

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

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

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

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