awesome-production-machine-learning

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

dolthub/dolt https://github.com/dolthub/dolt: dolt clone dolt pull dolt push dolt checkout dolt branch dolt commit dolt merge dolt blame dolt diff mgramin/awesome-db-tools > schema > changes: https://github.com/mgramin/awesome-db-tools#changes EthicalML/awesome-production-machine-learning#model-and-data-versioning: https://github.com/EthicalML/awesome-production-machine-lear...