Hacker News Reader

Is hyper-parameter optimization a cherry on top or one of the core value propositions?

IME hyper-parameter optimization doesn't require much in terms of implementation effort (e.g. [1]), but requires compute. I would be surprised if a professional ML/DS user were to seriously consider paying for the implementation of the optimization.

[1] https://people.eecs.berkeley.edu/~kjamieson/hyperband.html

Are there any similarities or distinctions between this article (link below) and how your system works to tune hyper-parameters?

https://blog.coast.ai/lets-evolve-a-neural-network-with-a-ge...

This article seems to discuss genetic algorithms which could be used for hyperparam optimization. We use another method called Bayesian (GP) hyperparam optimization. According to our internal benchmarks and academic research Bayesian methods outperform genetic algorithms. Another thing to keep in mind that we automate the entire process for you. You only need to provide a list of parameters you'd like to tune.

That service doesn't justify the price tag for me.