[westurner]

What are some adversarial cases for gradient descent, and/or what sort of e.g. DVC.org or W3C PROV provenance information should be tracked for a production ML workflow?

Gradient descent: https://en.wikipedia.org/wiki/Gradient_descent

Stochastic gradient descent: https://en.wikipedia.org/wiki/Stochastic_gradient_descent

Online machine learning: https://en.wikipedia.org/wiki/Online_machine_learning

adversarial gradient descent site:github.com inurl:awesome : https://www.google.com/search?q=awesome+adversarial+gradient...

https://github.com/EthicalML/awesome-production-machine-lear...

Robust machine learning: https://en.wikipedia.org/wiki/Robustness_(computer_science)#...

Robust gradient descent

[craigacp]

We built model & data provenance into our open source ML library, though it's admittedly not the W3C PROV standard. There were a few gaps in it until we built an automated reproducibility system on top of it, but now it's pretty solid for all the algorithms we implement. Unfortunately some of the things we wrap (notably TensorFlow) aren't reproducible enough due to some unfixed bugs. There's an overview of the provenance system in this reprise of the JavaOne talk I gave here https://www.youtube.com/watch?v=GXOMjq2OS_c. The library is on GitHub - https://github.com/oracle/tribuo.