3 years ago

Category: Security, Data, encryption, github, gitlab, automation, yaml, shell, bitbucket

Estimates vary that Machine Learning Engineer labor time of 5–15% is spent on the Machine Learning engine (MLLabOps). The other 85–95% is labor time spent on getting and munging data for input into Machine, pre-processing, the domain of DataOps, and putting and maintaining a stable version of the entire Machine Learning application (MLA) in production, the domain of MLProdOps.

The tools features are scored and summarized in a chart in the Summary section.

DevOps Script 2.5: Plugins must be written in Java and installed in Jenkins.

The total order of "lifecycle-cost" from highest rated to lowest is shaded in green: GitHub Action, CircleCi, devops.py, and Jenkins.