We often want to find the best settings for our systems, whether it’s configuring the best JVM parameters, optimizing user workflows, or selecting the right configuration for a machine learning algorithm. Black-box optimization techniques that can find good (hopefully optimal!) parameters have been investigated for the last 60 years, but over the last 20 years there’s been significant attention placed on creating versions that can take advantage of parallel compute.

In this talk, we’ll cover the types of real-world problems that are being solved with these techniques. We’ll do a deep dive into a few of the most popular ones, such as Distributed Nelder-Mead and Bayesian Optimization, and discuss their trade-offs. You should walk away with an understanding of what’s actually going on inside of these black-boxes and a good idea of how you can start applying them to your problems today.